Research and analysis

Exploring approaches to forecasting IP demand

Published 16 April 2024

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1. Executive Summary

1. Forecasting data on intellectual property rights (IPRs) is important for planning future resource requirements and revenues at IP offices, and for use as part of corporate reporting.

2. The UK Intellectual Property Office (UKIPO) applies a “bottom-up” forecasting approach, disaggregating IPR filings by applicant group (country of origin, representation, and technology type) to identify trends. Future trend scenarios by applicant group are selected through discussion with examination groups in the relevant business areas. Performance against forecasts is monitored through business intelligence reporting.

3. This paper identifies forecasting methods used by IP offices and other organisations based on literature available in the public domain, and information requested directly from the IP offices by the IPO. This exercise is undertaken to investigate additional options for future forecasting at the IPO.

4. The following methods are found to have been used or tested by the IP offices and other organisations reviewed:

i. Trend extrapolation

  • trend extrapolation has been used or tested by several IP offices (European Patent Office (EPO), United States Patent and Trademark Office (USPTO), Spanish Patent and Trademark Office (SPTO)) to forecast future patent applications based on their historic growth, typically using an autoregressive (AR) model
  • Hingley and Nicolas (2004) find AR models perform poorly under certain market conditions, such as financial crises or periods of rapid technological change[footnote 1], when IP filings fluctuate greatly from past trends. Reliance on this technique may therefore pose a risk for IP offices

ii. Improvements to the autoregressive (AR) model

  • using SPTO data, Hidaglo and Gabaly (2012) find that the autoregressive integrated moving average (ARIMA) model improves predictive accuracy compared to the simple AR model[footnote 2], by forecasting based on lagged moving averages to smooth out the influence of outliers
  • the EUIPO finds Vector Autoregression (VAR) improves upon ARIMA when forecasting EU trade mark (EUTM) and registered community designs (RCD). In VAR, a dynamic relationship between IP and economic time series is modelled, where all variables are jointly determined. VAR forecasting is also used by financial fa including the European Central Bank (ECB)[footnote 3]and International Monetary Fund (IMF)[footnote 4]
  • the Organisation for Economic Co-operation and Development (OECD) seeks to improve upon the simple AR model by including non-linearities such as discontinuities and structural breaks, relaxing the assumption of continuation of past trends[footnote 5], when producing its macroeconomic forecasts
  • the EPO relaxes the assumption of homogenous trends across applicant countries, and forecasts weights for regional blocs when forecasting subsequent EPO filings[footnote 6]. A similar approach could be used to account for industry differences in filing trends

iii. Explanatory variables

  • some IP offices use economic theory to select explanatory variables to include in their forecasting models. The EPO uses R&D expenditure to forecast patent filings[footnote 7], based on a production function for knowledge used in contemporary R&D-based endogenous growth models
  • using SPTO data, Hidalgo and Gabaly (2013) find controlling for GDP and the industrial production index improves predictive accuracy when forecasting patents and trade marks[footnote 8]
  • EUIPO (2023) find that controlling for industrial sector confidence improves EUTM forecasts and controlling for consumer confidence improves RCD forecasts Consumption and investment from National Accounts improves accuracy of both forecasts
  • on Swiss Federal Institute of Intellectual Property (IPI) data, Bock et al (2004) find controlling for the Dow Jones index and Swiss consumer confidence index in structural state-space models improves forecasting performance[footnote 9]
  • on EPO data, Hingley and Park (2016) find that controlling for the cyclical component of GDP can improve predictive accuracy of forecasting patent filings in the presence of shocks[footnote 10]
  • WIPO find no improvement in forecasts of PCT (patent cooperation treaty) filings when controlling for GDP, which may reflect that alternative filing routes hold a more complex relationship with changing economic conditions

iiii. Transfer function models

  • The EPO and World Intellectual Property Organisation (WIPO), as supranational patent offices, use domestic (‘first’) filings as an indicator of subsequent EPO and PCT filings[footnote 11]using a two-stage transfer function model[footnote 12]. This method may be less suited to national offices that attract fewer subsequent patent filings

iv. Survey data

  • the EPO and other organisations (including OECD) accompany forecasting models with annual survey data. Dannegger and Hingley (2013) find the EPO’s annual survey improves predictive accuracy for a forecasting horizon of one year before declining noticeably[footnote 13]. The EPO stopped their large-scale annual survey in 2021 but continue a smaller-scale pulse survey
  • potential non-response bias introduced by low response rates to surveys should be considered. The OECD reduces reliance on survey data in its macroeconomic forecasts by producing forecasts as a combination of “soft” indicators, such as consumer surveys, and “hard” indicators, including quantitative empirical data (industrial production, retail sales, house prices etc)[footnote 14]

vii. Expert judgement

  • use of expert panels is common across IP offices and other organisations that forecast. Experts intervene by interpreting survey data (USPTO, EPO), creating scenarios based on consideration of driving factors (EUIPO, EPO), and refining model forecasts to account for hard-to-predict events such as legislation changes (EPO, Euromonitor)
  • until 2007, the USPTO used the Delphi method to interpret survey data[footnote 15], whereby an expert panel adjusts their forecasts after each survey round, based on interpretation of the group survey response

viiii. Artificial intelligence (AI)

  • Havermans et al (2017)[footnote 16] find use of artificial intelligence (AI) forecasting techniques by the ETMDN (European Trade Mark and Design Network) improves forecasting accuracy compared to traditional methods. Instead of manual trial-and-error, algorithms select explanatory variables, parameters, lags and optimal transformations to best fit the data, and remove outliers. A new framework based on AI forecasting techniques has been implemented by 22 European IP offices under ETMDN
  • AI forecasting techniques are increasingly and successfully used in scientific, economic and business fields (such as energy, market research, big data, engineering, finance, business, biology, health, defence and robotics)[footnote 17]. Bayesian dynamic modelling has been used by private companies to forecast product sales[footnote 18]. Support vector regression modelling (SVR) has been used by energy companies to forecast energy demand[footnote 19]. Expectation maximisation (EM) has been used by Eurostat to select factors for forecasting unemployment, GDP and inflation[footnote 20]. Application of AI methods to IP forecasting is still a novel approach (Havermans et al, 2017)

ix. Error correction model (ECM)

  • an ECM estimates the speed at which a dependent variable returns to equilibrium (its long run trend) after a change in another variable, allowing modelling of response to shocks. The Department for Transport (DfT) uses an ECM to estimate elasticities of passenger demand for air travel with respect to price, income and GDP[footnote 21]
  • an ECM can only be used if a long run relationship exists between the dependent and independent variables (‘cointegration’). Josheski et al (2011) find evidence of cointegration between quarterly growth of patents and quarterly GDP growth[footnote 22], which may support use of an ECM. EUIPO (2023) conduct a Johansen Cointegration Test that rejects the presence of cointegration between trade mark and design filings and economic variables (confidence indicators, consumption and investment), evidence against use of VECM (vector error correction model)

ix. Online access to IP office forecasting tools

  • some IP offices have made their forecasting tools accessible and interactive online, to improve transparency. The USPTO’s Patent Pendency Model is accessible via an interactive spreadsheet on their website[footnote 23], and the trade mark and design forecasting tool developed by the TMDN (the European Trade Mark and Design Network), used by 22 European IP offices, is also available online[footnote 24]

2.  IPO forecasting approach

The current focus of the IPO’s forecasting effort is on capturing different behaviours within IPR filing data by disaggregating this into different applicant groups. This disaggregation is different depending on the intellectual property right being considered. Examples of disaggregation groupings include country of origin, representation, and technology type.

This approach allows a more meaningful exploration of changing trends than top-level analysis, and the creation of forecasts unique to the disaggregated groups of applicants. It also allows shocks to input among applicant groups to be identified, investigated, and reviewed to determine how best to incorporate into current understanding and future forecasts.

Using this disaggregated data, the forecast process extrapolates trends for these applicant groups and uses historic growth/shrinkage rates to produce a range of possible scenarios. These scenarios are discussed with the business areas responsible for delivering the examination process. This allows discussion and sharing of insights, which are reviewed alongside forecast options, to select the most appropriate option for each disaggregated group.

Business intelligence reporting is used to review performance against forecast and track input for the disaggregated groups. This allows regular monitoring of the input received by the IPO, to better understand the movements and their drivers, and act as a ‘warning system’ should input notably stray from forecast.

When creating input forecasts for patents, these are created for applications, searches, and exams to identify resource requirements and revenue streams associated with these different actions. Similarly, the IPO creates forecasts of renewal income for all rights to predict the monies received, considering likely renewals due, dropout rates, and when payments are made.

The IPO is keen to learn from others by investigating the use of alternative forecasting approaches, subject to being able to find suitable explanatory variables or relevant international data. Where appropriate, these alternative approaches will be incorporated as additional options for future forecasts.

3.  Approaches to forecasting: IP offices and organisations

IP Office / organisation  Forecasting methods used  Forecasting method(s) tested 
European Patent Office (EPO)   * Scenario planning
* Trend extrapolation
* Autoregressive integrated moving average (ARIMA) model
* Transfer model
* Annual survey		  * Econometric modelling 
European Union Intellectual Property Office (EUIPO)  * Historical trend projection
* ARIMA
* Vector Autoregressive model (VAR)
* Impulse response function (IRF)
* Scenario planning 		  * ITF (Intelligent Transfer Function) 
Spanish Patent and Trademark Office (SPTO)  * N/A (information could not be sourced)  * ARIMA
* Simple econometric model with a predictive lag variable
* Polynomial Distributed Lag (PDL) model
 * Intelligent Transfer Function (ITF) model
Federal Intellectual Property Institute of Switzerland (IPI) * Simple trend model
*ARIMA 		   * State-space model with explanatory variables 
United States Patent and Trademark Office (USPTO) * Microsoft Excel simulation tool (Patent Pendency Model)
* ARIMA
* Econometric models with explanatory variables
* Simple linear trend models
* Exponential smoothing models		  
World Intellectual Property Organisation (WIPO)  * Trend analysis
* Linear trend model
* ARIMA
* Transfer model 		 * Using economic indicators as explanatory variables
The European Trade Mark and Design Network (ETMDN) / Cooperation Fund project * Support Vector Machines (SVM)
* Artificial Neural Networks (ANN)
* Linear regression (LR)		  

European Patent Office (EPO)

Forecasting approach

  • to inform its annual budget, a set of scenarios for annual growth of patent applications are presented to the EPO’s management committee in February each year. By this time, previous-year figures have typically stabilised and can be input to the forecast model. A scenario is selected by the management committee following internal discussion of drivers, and this is then presented to governing bodies between May and June, to inform the end of year budget, approved in December
  • the lag between forecasting and budget approval can cause problems if perception of future market conditions change during the year (Hingley and Nicolas, 2004)[footnote 25], though the forecast is rarely adjusted. In addition to its annual forecast, the EPO forecasts over a 5-year horizon to inform financial planning.
  • scenario-based planning has been used by the EPO since 2019, prior to which a single forecast of patent application growth would be presented to the president of the EPO for sign-off. The practice of discussing scenarios is now preferred by the EPO’s management committee, as it allows consideration of current drivers
  • the forecast model used to produce growth scenarios at the EPO is a two-stage domestic patent transfer model. As a supranational patent office, the EPO mainly attracts subsequent filings, meaning applicants typically first file a patent application at their national patent office[footnote 26]. These ‘first’ filings are used to forecast subsequent EPO filings. In the first stage of the transfer model, first filings in Europe, Japan and the USA[footnote 27] are forecast using an autoregressive distributed lag (ADL) model based on economic growth theory and the knowledge production function. Transfer ratios of first filings into EPO patent applications are then forecast per bloc of origin using trend extrapolation (see Figure 1). In the second stage, subsequent EPO filings are forecast in an ADL model. This bases forecasts on domestic filings, previous filings at the EPO, size of the EPO market, and economic activity in Europe[footnote 28]. The transfer model is solved dynamically using an iterative method (1000 repetitions of the Gauss-Seidel Method)[footnote 29]. -in the past, the EPO has used the autoregressive integrated moving average (ARIMA) model to inform its patent application growth forecasts. This forecasts filings based on their historical movement, but unlike the simple autoregressive (AR) model, uses past moving averages of variables to smooth out the influence of outliers. This model is now only used by the EPO for cross-checking outputs of the transfer function model, and typically does not inform budget forecasts, as it is found to perform poorly in times of turbulence
  • until 2021, the EPO carried out an annual survey on applicants’ filing intentions, that had been run since 1996. 1,000 of the most active applicants, and 4,000 randomly selected applicants[footnote 30] were invited to participate, the random group selected such that applicants per bloc and filing type had equal probability of being selected[footnote 31]. The survey had a low response rate (35%)[footnote 32], and respondents’ filing intentions were found to serve as a poor indicator of future filings. As a result, the survey was stopped in 2021
  • the EPO continues to carry out a yearly smaller-scale pulse survey on patent filing intentions, most recently in Q4 2022 as part of its user satisfaction survey. Random sampling is conducted within four strata: technology field, user type (applicants and external representatives), country, and filing power (number of applications filed). 234 responses were received by telephone and online to the most recent survey (from 131 applicants and 103 external patent attorneys) Findings are presented to the EPO management committee alongside outputs of the transfer function model, when drawing up forecasting scenarios
  • further methods have been tested for use by the EPO. Hingley and Park (2016) tested a structural econometric model to forecast EPO patent filings amid cyclical shocks[footnote 33]. The model took a dynamic log-linear functional form, with components included to control for GDP and R&D spend by technological field. Filings were found to respond more to GDP fluctuations than to deviations in R&D spending from trend. Similar findings were made by Hingley and Park (2017), testing a dynamic model of patenting separating the cyclical component of GDP from its trend component, and finding that patent filings are strongly pro-cyclical[footnote 34]. These results suggest forecasting accuracy of models may be improved by controlling for the cyclical component of GDP. Analysis has been commissioned by the EPO to produce a 20-year forecast for patent applications using GDP as the main indicator. This is expected to be published next year

Evaluation

  • the model specification used to forecast domestic filings in the first stage of the EPO’s transfer model is informed by endogenous growth theory. Technological progress is assumed to be the driver of GDP growth[footnote 35], and is assumed to evolve according to a knowledge production function whereby research labour input is proxied by R&D expenditure. However, when tested empirically, Hingley and Park (2016) find that EPO patent filings are not sensitive to short run movements in R&D[footnote 36], which may reduce performance of the model
  • as a supranational office, the EPO mainly attracts subsequent filings[footnote 37]. National offices that receive fewer subsequent filings may find a transfer model unsuitable for forecasting
  • Hingley and Nicolas (2004) suggest that the EPO could forecast weights for different technology classes of patenting[footnote 38]. However, the EPO has previously avoided bottom-up forecasts by technology class on account of finding that these overestimate aggregate filings due to overlap across classes
  • low survey response rates, as observed by the EPO in their pre-2021 annual surveys, means findings may be affected by non-response bias. This occurs when respondents to the survey differ systematically to non-respondents. For example, applicants may be more likely to respond to the survey if they have intentions to file patent applications, leading to upward bias in forecasts
  • Dannegger and Hingley (2013)[footnote 39] examine the predictive accuracy of patent filing forecasts from annual surveys[footnote 40], using nine years of EPO survey data. They find that accuracy of forecasts based on survey data is highest for the first year after each survey’s base year and declines noticeably for the two following years. This implies that survey methods should not be used for longer term forecasts

European Union Intellectual Property Office (EUIPO)

Forecasting approach

  • the EUIPO forecasts registered EU trade mark (EUTM) and registered community design (RCD) filing volumes, to make decisions on future budget and staff planning. Its forecasting method has changed over the years, from historical trend projection to ARIMA modelling, to Vector Autoregressive (VAR) modelling used today. This advancement was largely a response to increased volatility.
  • between 2011 and 2015, the EUIPO used historical trends to forecast filings, applying an average historical growth rate of ~5% per year. In 2016 and until 2021, the EUIPO used an Autoregressive integrated moving average (ARIMA) model to forecast filings[footnote 41]. ARIMA models forecast based on moving averages of historic data, to smooth out the influence of outliers and to consider trends, cycles, and seasonality. This method performed well[footnote 42] until the shock of COVID-19 in 2020. The office then turned to using a vector autoregressive (VAR) model, to incorporate relevant economic variables into forecasting.
  • the EUIPO now uses a VAR model with economic explanatory variables to forecast EUTM and RCD filings[footnote 43] on a quarterly basis[footnote 44]. This model treats every variable as endogenous, explained by its past values and past values of other variables in the model, allowing dynamic behaviours to be described[footnote 45]
  • variables were selected for inclusion to the EUIPO’s VAR model by comparing filing trends to National Accounts (NA) indicators for domestic demand, consumption and investment, as well as confidence indicators (based on business and consumer surveys). 10 VAR models were estimated with different combinations of variables to select the one with best fit: a model with 8 endogenous variables and 2 lags. Variables include: EUTM filings, RCD filings, three confidence indicators (for industry, services and consumers), and 3 variables from National Accounts: private FCE (final consumption expenditure), NFCF (net fixed capital formation), and net capital transactions with RoW (rest of world)
  • granger causality tests were performed to better understand the relationship between the variables in the VAR model. EUTM filings were found to be most responsive to confidence indicators in the industry sector and net capital transactions with the rest of the world. Design filings were found most responsive to consumer confidence indicators and private final consumption expenditure
  • the EUIPO found their VAR model improved upon ARIMA for forecasting EUTM and RCD filings[footnote 46]. The presence of a cointegration relationship among the variables was rejected when tested, suggesting that a VAR model is appropriate, rather than a Vector Error Correction model (VECM).
  • to forecast how filings respond and evolve to a shock to one of the economic variables in their VAR model, the EUIPO uses an Impulse Response Function (IPF). A shock to industrial sector confidence is found to have a large effect on EUTM filings in Q1, decreasing in magnitude in Q2, but still holding a cumulative effect after 2 years. A shock to consumer confidence shows greatest impact on RCD filings in Q1 and Q2, and a cumulative impact after 2 years of the same size as the initial effect. This helps forecasters to understand how filings might respond during periods of increased economic volatility
  • typically, three scenarios of forecasts produced by VAR are presented to the EUIPO’s Executive Director, who makes the final decision on which scenario is selected for budget and planning purposes

Evaluation

  • Gabaly and Hidalgo (2017) evaluate forecasting methods on 1996-2015 EUIPO trade mark and design filing data, finding that VAR models, using explanatory variables such as R&D investment or economic growth, presented an improvement compared to the prediction and modelling power of classic forecasting techniques such as trend projections (using ARIMA), exponential smoothing or classic econometric methods[footnote 47]. Forecasting accuracy was also found to be improved using ITF (Intelligent Transfer Function), an intelligent optimisation algorithm used to select variables, parameters, lags, outliers and optimal transformations to best predict future values. This identified GDP growth as the best predictor of trade mark filing growth, from several economic, IP and business indicators
  • VAR models, as used by the EUIPO, are increasingly being used for forecasting purposes, by the European Central Bank[footnote 48], the International Monetary Fund (IMF)[footnote 49], the European Commission’s Directorate General for Economic and Financial Affairs (EC DG-ECFIN)[footnote 50], and the Organisation for Economic Cooperation and Development (OECD)[footnote 51]
  • one difficulty faced when using VAR models is that, as the number of parameters that must be estimated increases, degrees of freedom in the model decrease, which can reduce precision of estimates. It is therefore important to only include variables that hold explanatory power, making tests such as Granger causality, as performed by EUIPO, important. These tests identify a causal relationship between EUTM filings and RCD filings, implying further research is required to understand the interdependence of demand for different IPRs.
  • the Impulse Response Function (IRF) can be used to model different scenarios in the VAR model, for example identifying the impact on trade mark filings if consumer confidence drops to some level. Other offices may benefit from this to understand the potential impact of shocks.
  • other IP offices that receive a large proportion of international registrations from WIPO may benefit from using quarterly, as opposed to monthly filing data in their forecasts. EUIPO do so to avoid irregular seasonal and calendar effects that may result from lacking registration date for WIPO filing data

Spanish Patent and Trademark Office (SPTO)

Forecasting approach

  • research has been conducted for the SPTO to develop a methodology which predicts changes in the number of national patent and trade mark applications[footnote 52] for a time horizon of three years. Hidalgo and Gabaly (2012)[footnote 53] tested various methods on 1979-2009 SPTO data, including an exponential smoothing model (Holt type)[footnote 54], an auto-regressive model of order 1 (AR1), and an ARIMA model. These models were each found to explain at least 50% of the variation in trade mark applications, and at least 80% of variation in patent applications. Taking other metrics for goodness-of-fit into consideration (mean squared errors, Bayesian information criterion), the authors concluded that the ARIMA(1,1,0) model in natural logarithms is the most useful for forecasting, for both patent and trade mark application time series
  • in a later paper, (Hidalgo & Gabaly, 2013)[footnote 55],tested the ARIMA model against a predictive lag variable model, a polynomial distributed lag (PDL) model and an intelligent transfer function (ITF) model, on 2011-2014 SPTO data. They found all three models improved upon the prediction and modelling power of the ARIMA model, and in particular the ITF model surpasses all other models tested in terms of the degree of fit (fewer errors), improving the selection of predictors and optimizing the predictions which are obtained. Inclusion of GDP and the industrial production index as explanatory variables was also found to improve predictive accuracy

Evaluation

  • autoregressive models offer predictive accuracy if there is sufficient correlation between movement of current and past filings. However, several studies[footnote 56] have found that changes in the series of patents and trade mark applications tend to be largely influenced by milestones and regulatory changes. For example, in the case of Spanish patent applications there was a heavy decrease in the year of 1986 due to the change in Spanish legislation as a result of the enactment in Spain of the Munich European Patents Convention. This causes autoregressive models to suffer from estimation problems, as historical filing trends do not explain future changes caused by idiosyncratic factors
  • Hidalgo & Gabaly’s (2013) findings show that predictive accuracy of AR models can be improved by including other explanatory variables (GDP and the industrial production index) and using an ITF model to test the degree of fit for optimisation

Federal Intellectual Property Institute of Switzerland (IPI)

Forecasting approach

  • the IPI recently began using simple trend models and ARIMA models for its IP forecasting, led by a team of 3 economists
  • other forecasting methods have also been tested on IPI data. Bock et al (2004)[footnote 57] applied a structural state-space model to forecast trade mark applications received by IPI between 1992 and 2004. This model uses state, input and output variables to model a time series by a set of first-order differential equations. Explanatory variables, including the Dow Jones index and Swiss consumer confidence index, were also included in the model. It was found to perform better than an ARIMA model with trend, seasonal and random components, based on RMSE (Root Mean Square Error) and relative RMSE indicators. However, it failed to predict “extraordinary situations”, including a steep increase in applications received in the year 2000, associated with a decrease in application fees and the dot-com boom

Evaluation

  • Hingley and Nicolas (2004) find AR models perform poorly under certain market conditions, such as financial crises or periods of rapid technological change[footnote 58], when IP filings fluctuate greatly from past trends. Similarly, structural state-space models fail to predict changes driven by idiosyncratic factors, though they are found to offer improvement on forecasting relative to ARIMA modelling
  • evidence, based on Swiss data, suggests that inclusion of economic explanatory variables improves IP forecasting accuracy, replicating findings on data at other IP offices (EPO, EUIPO, SPTO, USPTO)

United States Patent and Trademark Office (USPTO)

Forecasting approach

  • the USPTO uses a simulation tool called the Patent Pendency Model (PMM)[footnote 59], implemented in Excel, to predict and simulate patent examination outcomes. The model forecasts using indicators on the supply and demand side. Supply indicators include historical data on manpower at the patent office (patent examiner hires, overtime worked, and examiner attrition rate). Demand indicators include filing forecasts (projections of annual patent application filings based upon forecasts and consensus judgments about the future) and RCE (request for continued examination) Filings. Model outputs are used to predict and simulate future workloads and resources in the examination processes, efficiency and performance
  • the USPTO forecasts application types separately (original filings, continuing type applications, PCT National Phase, Design filings, and Requests for Continued Examination (RCE), and Provisional filings).
  • the USPTO considers various econometric models when forecasting original filings. These include economic indicators as covariates, including GDP, unemployment data, and CPI
  • predictions of patent renewals (maintenance) utilises linear trend models based on historical patent renewal rates and the number of patents granted in prior years
  • Trade mark filings are forecast in a similar way to patent filings. Several econometric models are used to predict trademark filings using prior period filings and economic data such as GDP, unemployment, and CPI
  • future trademark renewals are predicted using a similar methodology as that of patent renewals. A simple linear trend is extrapolated based on historical renewal rates and trademarks registered in prior years
  • for design patent applications, a simple linear trend model is employed to predict future filing activity
  • the USPTO considers legislative or legal changes that could affect filings such as fee rate changes for USPTO services and significant changes to patent law/procedures.
  • until 2007, the USPTO used the Delphi method to inform forecasts. This is a method of survey data interpretation, whereby a panel of experts adjust their forecasts after each survey round to achieve a consensus forecast for patent filings based on group opinion

Evaluation

  • the USPTO makes its simulation tool, PPM, available online[footnote 60] through an interactive spreadsheet that can be accessed in excel. This may help patent applicants to understand how long their patent pendency period will last, based on supply and demand factors at the IP office.

World Intellectual Property Organisation (WIPO)

Forecasting approach

  • at WIPO, various models are employed to enhance the accuracy of forecasting PCT (Patent Cooperation Treaty) patent filings, Madrid trademark filings, and renewals, as well as Hague design filings and renewals. To forecast filings, a linear trend model and the ARIMA model are used to analyse monthly data. To forecast renewals, a transfer model is employed that leverages yearly registration data and historical renewal percentages across different renewal cycles
  • in addition to forecasting filing volumes, WIPO also predicts factors that have an impact on revenue, including the number of pages, the number of designs per application, and various fee reductions. By considering these factors, WIPO aims to provide accurate forecasts of revenue generation
  • WIPO’s forecasts are used internally by its finance and operational departments. Typically, financial targets are set below forecasted values, whereas operational targets (such as translation volume) are set slightly higher

Evaluation

  • WIPO presents its forecasts with a range that is based on an 80% confidence level. This approach allows users (finance and operational departments of WIPO) to make informed decisions by considering the range of possibilities. Users can then adjust their financial and operational targets, taking into account the forecasting results as well as any associated risk factors.
  • the inclusion of other economic indicators, such as GDP, as predictive variables was initially tested by WIPO, but ultimately discontinued due to the lack of improvement in model performance. WIPO’s systems provide an alternative filing route, making it challenging to establish a straightforward correlation between changing economic conditions and applicants’ filing strategies.

The European Trade Mark and Design Network (ETMDN) / Cooperation Fund project

Forecasting approach

  • the European Trade Mark and Design Network (ETMDN) connects the EUIPO with national EU IP offices and user associations[footnote 61]. In 2013, the network initiated a project to evaluate the best methods for forecasting trade mark and design filings (Havermans, Gabaly and Hidalgo, 2017). The working group consisted of experts from the EUIPO and national IP offices of Denmark, Hungary, Poland, Portugal, Spain, and the UK, with the EPO acting as an observer, and support from the Cooperation Fund[footnote 62].
  • various forecasting methods were tested on EUIPO trade mark and design filings from 1996 to 2015[footnote 63]. Linear regression (LR) and AI techniques (support vector machines (SVM) and artificial neural networks (ANN)) were found to outperform traditional forecasting approaches including trend extrapolation, exponential smoothing, and ARIMA techniques. These AI techniques use an algorithm to select the optimal model that best fits the data, employing intelligent optimisation: selecting variables, parameters, lags, outliers and optimal transformations to best predict future values. This was one of the first studies to apply these techniques to the IP-forecasting area.
  • findings informed the development of a new online forecasting tool, allowing the user to forecast trade mark and design filings by selecting a forecasting model (SVM, ANN, or LR), explanatory variables (GDP and/or unemployment growth) and a forecasting scenario (baseline, upside, or downside). The interface can be observed in figure 2
  • the forecasting tool was adopted by 22 EU member state IP offices. However, according to an EUIPO source, no office has yet used the model to forecast for budgeting purposes

Evaluation

  • AI pattern recognition and self-improving capabilities can improve the accuracy of forecasts compared to traditional methods. However, AI techniques may be regarded by some as a ‘black box’ approach. Algorithms can reach a high internal complexity in computational statistics terms, which can make understanding and interpretation by analysts difficult
  • AI forecasting does not require expert intervention for parameterisation and selection, as other modelling techniques do. This is because they are algorithms that are mainly based on automatable machine learning techniques, which automatically take the relevant decisions to improve the forecasting process. However, lack of human analytical understanding can reduce interpretability of outputs
  • for 2015, the SVM model predicted 108,028 EUIPO trade mark filings, compared to an actual of 108,028, giving an error of 0.4%. The average error across the three models (SVM, ANN and LR) in forecasting 2013-2015 trade mark and design filings was 1.6% and 2.6% respectively. In no case did one of the models consistently outperform the other three across all forecasting years (see Figures 3 and 4)[footnote 64]

4. Approaches to forecasting: Other selected organisations

Organisation Forecasting method(s) used
Thomson Reuters * Trend extrapolation
Patent Forecast * AI-based interactive visualisation
* Analyst intelligence
Department for Transport * Error correction model (ECM)
National Grid / Ofgem and energy sector * Scenario-based forecasting
* Support Vector Regression (SVR)
Eurostat * Time-series factor model
OECD * Indicator models
* Structural change analysis
Euromonitor * Scenario-based forecasting
* Industry forecast model based on elasticities

Thomson Reuters

Forecasting approach

  • Thomson Reuters is one of the few private organisations found to forecast patent filings. It uses extrapolation techniques based on average growth rates over the last five years to forecast future patent filing volumes for economic areas (including China, the EU, Japan, South Korea and the US) and uses this information to calculate total and domestic filing growth rates[footnote 65].

Evaluation

  • Thomson Reuters last updated their patent filing forecasts in 2010, according to publicly available information. Forecasting methods have since advanced beyond trend extrapolation, to forecast fluctuations in filings from their historical trend

Patent Forecast

Forecasting approach

  • Patent Forecast[footnote 66] is an independent company that offers interactive visualisation software of AI-sourced patent and market data, updated every week across 56 sectors. It uses a combination of AI and employee analysts to spot market shifts and industry trends in patent filings using its proprietary software. This evidence informs sector-specific forecasts published in articles on its website[footnote 67].

Evaluation

  • using AI to identify market shifts and industry trends may provide valuable anecdotal evidence to accompany forecasting models, in addition to evidence gathered from expert panels
  • speculation and use of anecdotal evidence reduces ease of forecasting relative to simplified models that can be clearly defined by their assumptions.

Department for Transport

Forecasting approach

  • The Department for Transport (DfT) runs the “National Air Passenger Demand Model (NAPDM)” [footnote 68], which forecasts passenger demand for air travel out to 2050 [footnote 69]. An Error Correction Model (ECM) is used to estimate elasticity of demand for air travel with respect to income, price (air fare), and measures of economic activity (such as GDP). This tells us how responsive demand is to a change in one of these variables
  • an ECM is used when dealing with cointegrated data, meaning that two time series (for example, air travel demand and consumer income) have a long-run relationship. Other examples of cointegrated relationships include stock prices and dividends, and consumption and income. The cointegration coefficient estimated within the ECM is used to estimate how long it might take air travel demand to return to its long-term trend following a short-term shock, for example to consumer income. Different lag structures can be tested to remove potential autocorrelation, which occurs when the error terms in the regression are not independent of one another

Evaluation

  • for an ECM to be used to forecast elasticities with respect to IP filings, a long-run relationship (cointegration) must be found between IP filings and some other time series variable. Josheski and Koteski (2011) find a positive relationship in the long run between quarterly growth of patents and quarterly GDP growth, using the ARDL bounds test[footnote 70]. This suggests the suitability of ECM for estimating elasticity of patent filings growth to a change in GDP
  • ECM estimates how long it will take a variable (e.g. patent filings) to return to its long-term relationship with another variable (e.g. GDP) from a disequilibrium position caused by some shock. Unlike most other models discussed in this paper, this model addresses forecasting accuracy in the face of idiosyncratic short term shocks
  • The ECM model can be adapted to account for structural breaks in the long-run relationship between the cointegrating variables. A dummy variable is included in the regression model that takes the value of one after the year of the structural break, and zero otherwise.

National Grid / Ofgem and energy sector

Forecasting approach

  • the National Grid is responsible for publishing energy demand forecasts under Ofgem’s rules. Accurate forecasts are critical for efficiency; underestimation of energy consumption can lead to power outage, and overestimation can lead to unused capacity and waste of capacity
  • previously, National Grid produced a single forecast of annual gas demand based on analysis of history and views of the future incorporating forecasts of economic growth, industry intelligence about new developments, new technologies and new connections to the gas network. More recently, National Grid adopted a scenario-based approach to forecasting. Scenario 1 (“Gone Green”) uses a bottom-up approach to calculate energy consumption consistent with renewable energy targets and CO2 reduction targets being met. Scenario 2 (“Slow Progression”) uses econometric modelling, forecasting demand based on a range of factors, including fuel prices, economic growth and number of households[footnote 71]
  • Abdelkader et al (2015) investigate the use of support vector regression (SVR) model to forecast energy demand[footnote 72]. This is a machine learning model that solves non-linear optimisation problems to identify the line of best fit. It differs from other machine learning models by looking at the extremes of datasets to draw a decision boundary. The decision model of SVR can be easily updated for new information, and prediction accuracy is found to be higher than other machine learning models (Zhang et al 2018).

Evaluation

  • the National Grid’s forecasting methods show how scenario-based forecasting can be advanced through use of econometric techniques within scenarios
  • use of machine learning models for forecasting (such as SVR) have proven successful in the energy sector, and so could be considered for forecasting IP filings

Eurostat

Forecasting approach

  • Eurostat, the statistical office of the European Union, forecasts the growth rate of unemployment, GDP and inflation using a time-series factor model, where the factors include macroeconomic variables of monthly and quarterly frequency[footnote 73]. An EM (Expectation Maximization) algorithm is used to estimate the common factors and the factor loadings (the weights and correlations between each variable and the factor)

Evaluation

  • Factor model techniques could be considered for forecasting IP filings, where relevant factors could be identified using machine learning practice, such as expectation maximization algorithm, as used by Eurostat.

OECD

Forecasting approach

  • OECD, an intergovernmental economic organisation with 38 member countries, forecasts quarterly GDP growth using indictor models[footnote 74]. These combine information from both “soft” indicators, such as business sentiment and consumer surveys, and “hard” indicators, such as industrial production, retail sales, house prices etc. and use is made of different frequencies of data and a variety of estimation techniques
  • OECD has also investigated use of machine learning[footnote 75] to identify non-linearities and structural change in macroeconomic data, to make forecasts. OECD find that a model with a functional form incorporating non-linearities (multiple interactions, discontinuities and structural breaks) performs better than a simple AR(1) benchmark model (OECD, 2020). Non-linearities are common in macroeconomic data. For example, growing house prices may signal strong GDP growth up until a given threshold, beyond which the bubble bursts and the economy may decelerate. The method of structural change analysis aims to address these challenges

Evaluation

  • combining quantitative and qualitative forecasting methods reduces reliance on survey data, which has been shown to have a short horizon of predictive accuracy[footnote 76].

Euromonitor

Forecasting approach

  • Euromonitor, a UK market research company, forecasts GDP and inflation using a macroeconomic model covering a number of scenarios. Category market sizes are projected by forecasting upside or downside pressures using income and price elasticities. Final forecasts are further refined by industry experts to account for hard-to-predict events, such as legislation changes or key company marketing campaigns[footnote 77]
  • Euromonitor’s industry forecast model[footnote 78] identifies “driver effects” by industry. The model calculates elasticity of retail volume sales in specific industries to a change in GDP per capita, product price, habit persistence and population growth, based on historical time series data. These drivers are then used for forecasting (in a similar way to use of factors in a factor model).
  • figure 5 outlines Euromonitor’s approach to forecasting

Evaluation

  • use of income and price elasticities to forecast upside and downside pressures could be replicated for forecasting IP filings, if these are found to be sensitive to customer changes in income and fees

Private companies

Forecasting approach

  • there is evidence of private companies using advanced forecasting methods to forecast product sales. Researchers at Sun Microsystems, a US computer manufacturer now owned by Oracle Corporation, use a Bayesian dynamic linear mixture model to forecast their product sales[footnote 79]. This is a machine learning model that uses unknown quantities estimated from noisy measurements. The state of the system evolves from one state (time t) to another (time t+1) according to a known linear transition equation, which can include random disturbances and intervention effects. State values are successively predicted given the knowledge of the past observations, and then updated upon the reception of the next observation.

Evaluation

  • Yelland and Lee (2003) find higher forecasting accuracy when using dynamic linear models compared to exponential smoothing. However, processing speed of machines running DLMs can be very long, as exhaustive examination of all possible model sequences for all periods is required. Therefore, use of DLMs may prove difficult for large datasets, such as historical IP data

Figure 1: European Patent Office transfer model by bloc [footnote 80]

Figure 2: TMDN trade mark and forecasting tool user interface [footnote 81]

Figure 3: Results of use of the TMDN trade mark and forecasting tool to forecast EUIPO trade mark filings [footnote 82]

Figure 4: Results of use of the TMDN trade mark and forecasting tool to forecast EUIPO design filings [footnote 83]

Figure 5: Illustration from Euromonitor website showing its process for updating macroeconomic forecasts for external events[footnote 84].

  1. Hingley P, Nicolas M. Methods for forecasting numbers of patent applications at the European Patent Office. World Patent Information 2004;26(3):191e204. See also: Harvey A. Estimating regression models with multiplicative heteroscedasticity. Econometrica 1976;44(3):461e5; Griliches Z. Patent statistics as economic indicators: a survey. Journal of Economic Literature 1990;28(4):1661e707; Adams K, Kim D, Joutz FL, Trost RP, Mastrogianis G. Modelling and forecasting U.S. patent application filings. Journal of Policy Modelling 1997 19(5):491e535. ↩

  2. Hidalgo A, Gabaly S (2012) Use of prediction methods for patent and trademark applications in Spain. World Patent Inf 34(1):19–35. ↩

  3. How to estimate a VAR after March 2020 (europa.eu)](https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2461~fe732949ee.en.pdf). ↩

  4. Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All? (imf.org). ↩

  5. “Forecasting methods and analytical tools”, OECD website (accessed July 2022), accessible here: https://www.oecd.org/economy/outlook/forecastingmethodsandanalyticaltools.htm. ↩

  6. Quirinus A. Havermans, Samuel Gabaly, Antonio Hidalgo, “Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices’ events”, World Patent Information, Volume 48, 2017. ↩

  7. Used by the EPO to forecast patent filings at domestic European IP offices in the first stage of its transfer model to forecast subsequent EPO filings. ↩

  8. “Optimization of prediction methods for patents and trademarks in Spain through the use of exogenous variables”, Hidalgo and Gabaly (2013).   ↩

  9. Bock, Christian, Günter, Matthias, Haftka, Urs, Friedli, Thomas K., Mitter, Mike and Hüsler, Jürg, (2004), Forecast of trademark applications in Switzerland, World Patent Information, 26, issue 4, p. 275-282. ↩

  10. “Forcasting patent filings at the European Patent Office (EPO) with a Dynamic Log Linear Regression Model: Applications and Extensions”, Hingley and Park (2016). ↩

  11. Article 4 of the Paris Convention specifies that a Subsequent Filing for an invention should take place within one year of a First Filing. ↩

  12. Quirinus A. Havermans, Samuel Gabaly, Antonio Hidalgo, “Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices’ events”, World Patent Information, Volume 48, 2017. ↩

  13. Dannegger and Hingley (2013), “Predictive accuracy of survey-based forecasts for numbers of filings at the European Patent Office”. World Patent Information, Volume 35, Issue 3, 2013. ↩

  14. “Forecasting methods and analytical tools”, OECD website (accessed July 2022), accessible here: https://www.oecd.org/economy/outlook/forecastingmethodsandanalyticaltools.htm. ↩

  15. Ibid. ↩

  16. Quirinus A. Havermans, Samuel Gabaly, Antonio Hidalgo, “Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices’ events”, World Patent Information, Volume 48, 2017. ↩

  17. Ibid. ↩

  18. Forecasting Product Sales with Dynamic Linear Mixture Models. Sun Microsystems Laboratories Technical Report. ↩

  19. S. Abdelkader, K. Grolinger and M.A.M. Capretz (2015), “Predicting Energy Demand Peak Using M5 Model Trees”. Electrical and Computer Engineering Publications. Paper 72. ↩

  20. “Economic Forecasting: Models, indicators and data needs”, Eurostat (2003), accessible here: https://ec.europa.eu/eurostat/documents/3888793/5810497/KS-AN-01-002-EN.PDF.pdf/e40bff2c-19c7-4302-92f5-7e3d0c4921f3?t=1414778624000. ↩

  21. Econometric models to estimate demand elasticities for the National Air Passenger Demand Model (publishing.service.gov.uk). ↩

  22. Josheski, Dushko and Koteski, Cane, The Causal Relationship between Patent Growth and Growth of GDP with Quarterly Data in the G7 Countries: Cointegration, ARDL and Error Correction Models (September 3, 2011). Available at SSRN:  http://dx.doi.org/10.2139/ssrn.1921908. ↩

  23. See https://www.uspto.gov/learning-and-resources/statistics/patent-pendency-model ↩

  24. See press release here: Forecasting tool goes live - news (tmdn.org) and overview of tool here: euipn-tools—benefits–key-features—short-version—web-pages-resolution.pdf (ipkey.eu). ↩

  25. Ibid. ↩

  26. Article 4 of the Paris Convention specifies that a Subsequent Filing for an invention should take place within one year of a First Filing. ↩

  27. “Forecasting the number of European patent applications at the EPO”, presentation by the EPO Controlling Office in Munich, 2002. Available here: https://www.oecd.org/science/inno/33882754.pdf. ↩

  28. “Forecasting Patent Applications at the European Patent Office: A Bottom-Up Versus Top-Down Approach” Prepared for WIPO-OECD Workshop (2004). See https://www.oecd.org/science/inno/33879475.pdf. ↩

  29. Ibid. ↩

  30. More information on the EPO’s applicant survey can be found here: https://www.epo.org/service-support/contact-us/surveys/patent-filings.html. ↩

  31. “Forecasting the number of European patent applications at the EPO”, presentation by the EPO Controlling Office in Munich, 2002. Available here: https://www.oecd.org/science/inno/33882754.pdf. ↩

  32. Ibid. ↩

  33. “Forcasting patent filings at the European Patent Office (EPO) with a Dynamic Log Linear Regression Model: Applications and Extensions”, Hingley and Park (2016). ↩

  34. Hingley and Park (2017). “Do business cycles affect patenting? Evidence from European Patent Office filings”, Hingley and Park (2017). ↩

  35. The most recent advancement of the endogenous growth theory has been the emergence of R&D-based models of growth in the seminal papers of Romer (1990), Gossman and Helpman (1991a, 1991b) and Aghion and Howitt (1992). This class of models agrees with the neoclassical Solow model that capital is subject to diminishing returns, and hence accumulation of capital does not sustain growth in the long run. Instead, technological progress is the source of sustained long run growth in these models. See https://www.oecd.org/science/inno/33879475.pdf. ↩

  36. “Forecasting patent filings at the European Patent Office (EPO) with a Dynamic Log Linear Regression Model: Applications and Extensions”, Hingley and Park (2016). It is speculated in this paper that patentable inventions may be more likely a function of long run R&D programs, rather than being influenced by short run boosts or declines in R&D funding. ↩

  37. “Methods for forecasting numbers of patent applications at the European Patent Office”, Hingley and Nicolas (2004). Accessible here: https://www.researchgate.net/publication/222432969_Methods_for_forecasting_numbers_of_patent_applications_at_the_European_Patent_Office. ↩

  38. Ibid. ↩

  39. Dannegger and Hingley (2013), “Predictive accuracy of survey-based forecasts for numbers of filings at the European Patent Office”. World Patent Information, Volume 35, Issue 3, 2013. ↩

  40. Ibid. ↩

  41. See 2023_Modelling_and_forecasting_European_trade_mark_and_design_filings_FullR_en.docx (europa.eu) and Havermans, Gabaly and Hidalgo (2017). “Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices’ events”. ↩

  42. Except in 2016, when the result of the referendum on the withdrawal of the UK from the EU required an intervention model in the EUTM time series. ↩

  43. 2023_Modelling_and_forecasting_European_trade_mark_and_design_filings_FullR_en.docx (europa.eu). ↩

  44. A significant share of filings (20% of EUTM and 15% of RCD filings in 2021) are international registrations from WIPO, assigned to the month they were received at the EUIPO, and not the month they were registered at WIPO, creating irregular seasonal and calendar effects. This reduces the effectiveness of using monthly data. ↩

  45. The VAR model forecasts using ‘differenced’ variables rather than levels. This creates stationary variables, giving some protection against changing mean levels and reducing instabilities. The variables are also transformed with logarithms to stabilise variance. ↩

  46. 2023_Modelling_and_forecasting_European_trade_mark_and_design_filings_FullR_en.docx (europa.eu). ↩

  47. Quirinus A. Havermans, Samuel Gabaly, Antonio Hidalgo, “Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices’ events”,World Patent Information, Volume 48, 2017. This finding was also made on SPTO data by Hidalgo and Gabaly (2013). ↩

  48. Quirinus A. ↩

  49. Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All? (imf.org). ↩

  50. VAR MODELLING OF THE EURO AREA GDP ON THE BASIS OF PRINCIPAL COMPONENT ANALYSIS (europa.eu). ↩

  51. 38806703.pdf (oecd.org). ↩

  52. Three time series for trade marks were modelled: trade mark applications for products, trade mark applications for services, and total trade mark applications (products, services and unclassified). ↩

  53. Hidalgo A, Gabaly S (2012) Use of prediction methods for patent and trademark applications in Spain. World Patent Inf 34(1):19–35. Accessible at: https://www.sciencedirect.com/science/article/abs/pii/S0172219011001426. ↩

  54. The Holt type model forecasts based on level and trend, but not based on seasonality. Both level and trend components of the model are updating equations. ↩

  55. “Optimization of prediction methods for patents and trademarks in Spain through the use of exogenous variables”, Hidalgo and Gabaly (2013). Accessible at: https://www.sciencedirect.com/science/article/pii/S0172219012002104 ↩

  56. Harvey A. Estimating regression models with multiplicative heteroscedasticity. Econometrica 1976;44(3):461e5; Griliches Z. Patent statistics as economic indicators: a survey. Journal of Economic Literature 1990;28(4):1661e707; Adams K, Kim D, Joutz FL, Trost RP, Mastrogianis G. Modeling and forecasting U.S. patent application filings. Journal of Policy Modelling 1997 19(5):491e535; Hingley P, Nicolas M. Methods for forecasting numbers of patent applications at the European Patent Office. World Patent Information 2004;26(3):191e204. ↩

  57. Bock, Christian, Günter, Matthias, Haftka, Urs, Friedli, Thomas K., Mitter, Mike and Hüsler, Jürg, (2004), Forecast of trademark applications in Switzerland, World Patent Information, 26, issue 4, p. 275-282. See https://econpapers.repec.org/article/eeeworpat/v_3a26_3ay_3a2004_3ai_3a4_3ap_3a275-282.htm. ↩

  58. Hingley P, Nicolas M. Methods for forecasting numbers of patent applications at the European Patent Office. World Patent Information 2004;26(3):191e204. See also: Harvey A. Estimating regression models with multiplicative heteroscedasticity. Econometrica 1976;44(3):461e5; Griliches Z. Patent statistics as economic indicators: a survey. Journal of Economic Literature 1990;28(4):1661e707; Adams K, Kim D, Joutz FL, Trost RP, Mastrogianis G. Modelling and forecasting U.S. patent application filings. Journal of Policy Modelling 1997 19(5):491e535. ↩

  59. See https://www.uspto.gov/learning-and-resources/statistics/patent-pendency-model. ↩

  60. Ibid (See FN 39). ↩

  61. European Trade Mark and Design Network - About the Network (tmdn.org). ↩

  62. Five years of the Cooperation Fund - World Trademark Review. ↩

  63. Quirinus A. Havermans, Samuel Gabaly, Antonio Hidalgo, “Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices’ events”,World Patent Information, Volume 48, 2017. ↩

  64. “An overview of EUIPN online tools, presentation to IP office, Soriano, Uzairi and Beckman (2016). Presentation available here: https://ipkey.eu/sites/default/files/legacy-ipkey-docs/euipn-tools—benefits–key-features—short-version—web-pages-resolution.pdf. ↩

  65. Zhou EY, Stembridge B. Patented in China. The present and future state of innovation in China. Thomson Reuters; 2010. Accessible here: https://manu56.magtech.com.cn/kxgc/EN/abstract/abstract487.shtml. ↩

  66. https://www.patentforecast.com/#:~:text=Patent%20Forecast%C2%AE%20is%20a,data%20that%27s%20updated%20every%20week. ↩

  67. See https://www.patentforecast.com/insights/. ↩

  68. Econometric models to estimate demand elasticities for the National Air Passenger Demand Model (publishing.service.gov.uk). ↩

  69. 2017 UK aviation forecasts can be found here: UK aviation forecasts 2017 - GOV.UK (www.gov.uk). ↩

  70. Josheski, Dushko and Koteski, Cane, The Causal Relationship between Patent Growth and Growth of GDP with Quarterly Data in the G7 Countries: Cointegration, ARDL and Error Correction Models (September 3, 2011). Available at SSRN:  http://dx.doi.org/10.2139/ssrn.1921908. ↩

  71. National Grid (November 2016), “Gas demand forecasting methodology”. Accessible here: https://www.nationalgrid.com/gas-transmission/document/132516/download and Microsoft Word - Gas Demand Forecasting Methodology Feb12.doc (nationalgrid.com). ↩

  72. S. Abdelkader, K. Grolinger and M.A.M. Capretz (2015), “Predicting Energy Demand Peak Using M5 Model Trees”. Electrical and Computer Engineering Publications. Paper 72. ↩

  73. “Economic Forecasting: Models, indicators and data needs”, Eurostat (2003), accessible here: https://ec.europa.eu/eurostat/documents/3888793/5810497/KS-AN-01-002-EN.PDF.pdf/e40bff2c-19c7-4302-92f5-7e3d0c4921f3?t=1414778624000. ↩

  74. “Forecasting methods and analytical tools”, OECD website (accessed July 2022), accessible here: https://www.oecd.org/economy/outlook/forecastingmethodsandanalyticaltools.htm. ↩

  75. “Adaptive trees: A new approach to economic forecasting”, OECD (2020), accessible here: https://www.oecd-ilibrary.org/economics/adaptive-trees-a-new-approach-to-economic-forecasting_5569a0aa-en. ↩

  76. Dannegger and Hingley (2013), “Predictive accuracy of survey-based forecasts for numbers of filings at the European Patent Office”. World Patent Information, Volume 35, Issue 3, 2013. ↩

  77. Euromonitor website, “Forecasting”, accessible here: https://www.euromonitor.com/our-methodologies/forecasting. ↩

  78. See https://www.euromonitor.com/video/introducing-the-industry-forecast-model. ↩

  79. Forecasting Product Sales with Dynamic Linear Mixture Models. Sun Microsystems Laboratories Technical Report. See https://www.jstor.org/stable/40599433. ↩

  80. Source: “Forecasting the number of European patent applications at the EPO”, presentation by the EPO Controlling Office in Munich, 2002. Available here: https://www.oecd.org/science/inno/33882754.pdf. ↩

  81. Source: “An overview of EUIPN online tools, presentation to IP office, Soriano, Uzairi and Beckman (2016). Presentation available here: https://ipkey.eu/sites/default/files/legacy-ipkey-docs/euipn-tools—benefits–key-features—short-version—web-pages-resolution.pdf. ↩

  82. Ibid. ↩

  83. Ibid. ↩

  84. Source: Euromonitor website, “Forecasting”, accessible here: https://www.euromonitor.com/our-methodologies/forecasting. ↩

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