Global models for time series forecasting: A Simulation study

Abstract

The recent advances in Big Data have opened up the opportunity to develop competitive Global Forecasting Models (GFM) that simultaneously learn from many time series. Although, the concept of series relatedness has been heavily exploited with GFMs to explain their superiority over local statistical benchmarks, this concept remains largely under-investigated in an empirical setting. Hence, this study attempts to explore the factors that affect GFM performance, by simulating a number of datasets having controllable characteristics. The factors being controlled are along the homogeneity/heterogeneity of series, the complexity of patterns in the series, the complexity of forecasting models, and the lengths/number of series. We simulate time series from simple Data Generating Processes (DGP), such as Auto Regressive (AR), Seasonal AR and Fourier Terms to complex DGPs, such as Chaotic Logistic Map, Self-Exciting Threshold Auto-Regressive and Mackey-Glass Equations. We perform experiments on these datasets using Recurrent Neural Networks (RNN), Feed-Forward Neural Networks, Pooled Regression models and Light Gradient Boosting Models (LGBM) built as GFMs, and compare their performance against standard statistical forecasting techniques. Our experiments demonstrate that with respect to GFM performance, relatedness is closely associated with other factors such as the availability of data, complexity of data and the complexity of the forecasting technique used. Also, techniques such as RNNs and LGBMs having complex non-linear modelling capabilities, when built as GFMs are competitive methods under challenging forecasting scenarios such as short series, heterogeneous series and having minimal prior knowledge of the data patterns.