Abstract:Paper aims: In this study, effective strategies to combine and select forecasting methods are proposed. In the selection strategy, the best performing forecasting method from a pool of methods is selected based on its accuracy, whereas the combination strategies are based on the mean methods' outputs and on the methods' accuracy. Originality: Despite the large amount of work in this area, the actual literature lacks of selection and combination strategies of forecasting methods for dealing with intermittent ti… Show more
“…In order to achieve good forecasting accuracy, it is important to use an appropriate forecasting strategy. Research on forecasting strategies have been long in the focus of numerous researchers (Bates and Granger 1969;Makridakis 1988;Bunn 1989;De Menezes et al 2000;Armstrong 2001;Timmermann 2006;Hall and Mitchell 2007;Clark and McCracken 2009;Geweke and Amisano 2011;Kourentzes et al 2014;Fildes and Petropoulos 2015;Nowotarski et al 2016;Pinar et al 2017;Kourentzes et al 2019;Galvão Bandeira et al 2020;Giacalone 2021;Kang et al 2021). In a seminal study on strategies about improving the forecasts accuracy, Bates and Granger (1969) confirmed that combining the forecasts using different models, instead of relying on the individual models, can improve the accuracy of predictions.…”
Section: Strategiesmentioning
confidence: 99%
“…For this purpose, different selection criteria were used. Galvão Galvão Bandeira et al (2020) stated that the selection can be based on the time series characteristics (Petropoulos et al 2018), the forecasting model performance (Wang and Petropoulos 2016;Fildes and Petropoulos 2015), the information criteria (Qi and Zhang 2001), or the judgmental expert selection (Petropoulos et al 2018). Kourentzes et al (2014) proposed a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy.…”
The primary purpose of the paper is to enable deeper insight into the measurement of economic forecast accuracy. The paper employs the systematic literature review as its research methodology. It is also the first systematic review of the measures of economic forecast accuracy conducted in scientific research. The citation-based analysis confirms the growing interest of researchers in the topic. Research on economic forecast accuracy is continuously developing and improving with the adoption of new methodological approaches. An overview of the limits and advantages of the methods used to assess forecast accuracy not only facilitate the selection and application of appropriate measures in future analytical works but also contribute to a better interpretation of the results. In addition to the presented advantages and disadvantages, the chronological presentation of methodological development (measures, tests, and strategies) provides an insight into the possibilities of further upgrading and improving the methodological framework. The review of empirical findings, in addition to insight into existing results, indicates insufficiently researched topics. All in all, the results presented in this paper can be a good basis and inspiration for creating new scientific contributions in future works.
“…In order to achieve good forecasting accuracy, it is important to use an appropriate forecasting strategy. Research on forecasting strategies have been long in the focus of numerous researchers (Bates and Granger 1969;Makridakis 1988;Bunn 1989;De Menezes et al 2000;Armstrong 2001;Timmermann 2006;Hall and Mitchell 2007;Clark and McCracken 2009;Geweke and Amisano 2011;Kourentzes et al 2014;Fildes and Petropoulos 2015;Nowotarski et al 2016;Pinar et al 2017;Kourentzes et al 2019;Galvão Bandeira et al 2020;Giacalone 2021;Kang et al 2021). In a seminal study on strategies about improving the forecasts accuracy, Bates and Granger (1969) confirmed that combining the forecasts using different models, instead of relying on the individual models, can improve the accuracy of predictions.…”
Section: Strategiesmentioning
confidence: 99%
“…For this purpose, different selection criteria were used. Galvão Galvão Bandeira et al (2020) stated that the selection can be based on the time series characteristics (Petropoulos et al 2018), the forecasting model performance (Wang and Petropoulos 2016;Fildes and Petropoulos 2015), the information criteria (Qi and Zhang 2001), or the judgmental expert selection (Petropoulos et al 2018). Kourentzes et al (2014) proposed a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy.…”
The primary purpose of the paper is to enable deeper insight into the measurement of economic forecast accuracy. The paper employs the systematic literature review as its research methodology. It is also the first systematic review of the measures of economic forecast accuracy conducted in scientific research. The citation-based analysis confirms the growing interest of researchers in the topic. Research on economic forecast accuracy is continuously developing and improving with the adoption of new methodological approaches. An overview of the limits and advantages of the methods used to assess forecast accuracy not only facilitate the selection and application of appropriate measures in future analytical works but also contribute to a better interpretation of the results. In addition to the presented advantages and disadvantages, the chronological presentation of methodological development (measures, tests, and strategies) provides an insight into the possibilities of further upgrading and improving the methodological framework. The review of empirical findings, in addition to insight into existing results, indicates insufficiently researched topics. All in all, the results presented in this paper can be a good basis and inspiration for creating new scientific contributions in future works.
“…The work of (Bandeira et al, 2020) proposes a forecasting application strategy considering two procedures: the combination of state-of-the-art forecasting methods and the selection of forecasting methods based on the accuracy of the models. The authors propose two combination strategies: simple mean and weighted mean based on the accuracy of the methods.…”
Paper aims: This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression.Originality: Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets.Research method: Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values.
Main findings:The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data.
Implications for theory and practice:This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.
“…Demand forecasting is one of the critical activities of supply chain management (Crum & Palmatier, 2003). As it refers to predicting future sales, demand forecasts support managerial decisions and operational planning throughout the supply chain (Bandeira et al, 2020). For instance, Sales and Operations Planning (S&OP) recognizes demand forecasts as an essential input for the process (Seeling et al, 2019).…”
Paper aims: Several concerns regarding the lack of interpretability of machine learning models obstruct the implementation of machine learning projects as part of the demand forecasting process. This paper presents a methodology to support the introduction of machine learning into the forecasting process of a traditional direct sales company by providing explanations for the otherwise obscure results. We also suggest incorporating human knowledge inside the machine learning pipeline as an essential part of capturing the business logic and integrating machine learning into the existing processes.Originality: Using explainable machine learning methods on real-life company data demonstrates that machine learning techniques are functional beyond the academy and can be introduced to everyday companies' production.
Research method:The project used real-world data from a company and followed a traditional machine learning pipeline to collect, preprocess, select and train a machine learning model, to conclude with the explanation of the model results through the implementation of SHAP Main findings: The results provided insights regarding the contribution of the features to the forecast. We analyzed individual predictions to understand the behavior of different variables, proving helpful when interpreting complex machine learning models.
Implications for theory and practice:This study contributes to a discussion about adopting new technology and implementing machine learning models for demand forecasting. The methodology presented in this paper can be used to implement similar projects on interested companies.
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