On the selection of forecasting accuracy measures

A lot of controversy exists around the choice of the most appropriate error measure for assessing the performance of forecasting methods. While statisticians argue for the use of measures with good statistical properties, practitioners prefer measures that are easy to communicate and understand. Moreover, researchers argue that the loss-function for parameterizing a model should be aligned with how the post-performance measurement is made. In this paper we ask: Does it matter? Will the relative ranking of the forecasting methods change significantly if we choose one measure over another? Will a mismatch of the in-sample loss-function and the out-of-sample performance measure decrease the performance of the forecasting models? Focusing on the average ranked point forecast accuracy, we review the most commonly-used measures in both the academia and practice and perform a large-scale empirical study to understand the importance of the choice between measures. Our results suggest that there are only small discrepancies between the different error measures, especially within each measure category (percentage, relative, or scaled)

Cross-temporal aggregation:Improving the forecast accuracy of hierarchical electricity consumption

Achieving high accuracy in energy consumption forecasting is critical for improving energy management and planning. However, this requires the selection of appropriate forecasting models, able to capture the individual characteristics of the series to be predicted, which is a task that involves a lot of system and region level, not only the model selection problem is expanded to multiple time series, but we also require aggregation consistency of the forecasts across levels. Although hierarchical forecasting, such as the bottom-up, the top-down, and the optimal reconciliation methods, can address the aggregation consistency concerns, it does not resolve the model selection uncertainty. To address this issue, we rely on Multiple Temporal Aggregation (MTA), which has been shown to mitigate the model selection problem for low-frequency time series. We propose a modification of the Multiple Aggregation Prediction Algorithm, a special implementation of MTA, for high-frequency time series to better handle the undesirable effect of seasonality shrinkage that MTA implies and combine it with conventional cross-sectional hierarchical forecasting. The impact of incorporating temporal aggregation in hierarchical forecasting is empirically assessed using a real data set from five bank branches. We show that the proposed MTA approach, combined with the optimal reconciliation method, demonstrates superior accuracy, aggregation consistency, and reliable automatic forecasting.</p

On the selection of forecasting accuracy measures

A lot of controversy exists around the choice of the most appropriate error measure for assessing the performance of forecasting methods. While statisticians argue for the use of measures with good statistical properties, practitioners prefer measures that are easy to communicate and understand. Moreover, researchers argue that the loss-function for parameterizing a model should be aligned with how the post-performance measurement is made. In this paper we ask: Does it matter? Will the relative ranking of the forecasting methods change significantly if we choose one measure over another? Will a mismatch of the in-sample loss-function and the out-of-sample performance measure decrease the performance of the forecasting models? Focusing on the average ranked point forecast accuracy, we review the most commonly-used measures in both the academia and practice and perform a large-scale empirical study to understand the importance of the choice between measures. Our results suggest that there are only small discrepancies between the different error measures, especially within each measure category (percentage, relative, or scaled)

Gamification of The Future: An Experiment on Gamifying Education of Forecasting

In this study, we developed a gamied learning platform called F-LauReLxp that employed three gamification strategies (called Horses for Courses, JudgeIt and Metrics to Escape) to help educate statistical, judgmental forecasting and forecasting accuracy respectively. This study presents a quantitative analysis of experimental design concerning learning performance of 261 students of an undergraduate and a MBA course. Treatment and control groups were compared in a series of experiments. The results show that using gamified applications as a complementary teaching tool in a forecasting course had a positive impact on students’ learning performance

Improving the forecasting performance of temporal hierarchies

Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias

Hierarchical forecast reconciliation with machine learning

Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down methods, focus on a particular aggregation level to anchor the forecasts. During the past decades, these have been replaced by a variety of linear combination approaches that exploit information from the complete hierarchy to produce more accurate forecasts. However, the performance of these combination methods depends on the particularities of the examined series and their relationships. This paper proposes a novel hierarchical forecasting approach based on machine learning that deals with these limitations in three important ways. First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches. Second, it structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence. Finally, due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns

Déjà vu: A data-centric forecasting approach through time series cross-similarity

Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, statistical models usually presume some data generation process while making strong assumptions about the errors. In this paper, we present a novel data-centric approach -- `forecasting with similarity', which tackles model uncertainty in a model-free manner. Existing similarity-based methods focus on identifying similar patterns within the series, i.e., `self-similarity'. In contrast, we propose searching for similar patterns from a reference set, i.e., `cross-similarity'. Instead of extrapolating, the future paths of the similar series are aggregated to obtain the forecasts of the target series. Building on the cross-learning concept, our approach allows the application of similarity-based forecasting on series with limited lengths. We evaluate the approach using a rich collection of real data and show that it yields competitive accuracy in both points forecasts and prediction intervals