The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting

Fotios Petropoulos and Evangelos Spiliotis
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Fotios Petropoulos: School of Management, University of Bath, Bath BA2 7AY, UK
Evangelos Spiliotis: Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece

Forecasting, 2021, vol. 3, issue 3, 1-20

Abstract: Forecasting is a challenging task that typically requires making assumptions about the observed data but also the future conditions. Inevitably, any forecasting process will result in some degree of inaccuracy. The forecasting performance will further deteriorate as the uncertainty increases. In this article, we focus on univariate time series forecasting and we review five approaches that one can use to enhance the performance of standard extrapolation methods. Much has been written about the “wisdom of the crowds” and how collective opinions will outperform individual ones. We present the concept of the “wisdom of the data” and how data manipulation can result in information extraction which, in turn, translates to improved forecast accuracy by aggregating (combining) forecasts computed on different perspectives of the same data. We describe and discuss approaches that are based on the manipulation of local curvatures (theta method), temporal aggregation, bootstrapping, sub-seasonal and incomplete time series. We compare these approaches with regards to how they extract information from the data, their computational cost, and their performance.

Keywords: information; combination; uncertainty; theta; temporal aggregation; bagging; sub-seasonal series (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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