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

Fotios Petropoulos,Evangelos Spiliotis

doi:10.3390/forecast3030029

Fotios Petropoulos, Evangelos Spiliotis

Open Access

https://doi.org/10.3390/forecast3030029

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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.

Highlights

Univariate time series forecasting is the creation of extrapolations for a single variable based on past, time-ordered observations of the same variable
Univariate time series forecasting can be challenging, especially since real life data do not comply with the assumptions and do not follow data generating processes usually assumed by models that can be found implemented in the forecasting support systems
We reviewed five approaches that can enhance the performance of univariate time series forecasting methods

Summary

Introduction

Univariate time series forecasting is the creation of extrapolations for a single variable based on past, time-ordered observations of the same variable. Examples include ForecastPro®, SAS Forecasting Server®, and the forecast package for R statistical software Such software and packages offer tools for batch and automatic forecasting with minimal to zero manual input. They integrate families of models, like exponential smoothing [2] and autoregressive integrated moving average, ARIMA [3], that can capture a wide range of data patterns and produce extrapolations with ease. Such families of models rely on assumptions that are barely met in practice, and struggle to select the most appropriate model for a given time series due to the uncertainties involved: identifying the optimal model form, estimating the optimal set of parameters, and dealing with the inherent uncertainty in the data [4]

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