Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series

Throughout the years, research in neural networks (NN) for univariate time series forecasting has received considerable attention. Recent developments have been mainly around preprocessing techniques such as deseasonalization and detrending to supplement the NN's learning process, and novel NN architectures such as recurrent neural networks, echo state networks, generalized regression neural networks and ensemble architectures to uplift the constraints of the conventional NN architecture (Nelson et al., 1999; Zhang and Qi, 2005; Ilies et al., 2007; Rahman et al., 2016; Yan, 2012; Zimmermann et al., 2012). However, in the time series forecasting community there has also been the longstanding consensus that simple methods will often outperform more sophisticated ones. This was a conclusion of the influential M3 forecasting competition held in 1999 (Makridakis and Hibon, 2000). So, complex methods are often viewed poorly in this field, and this has been especially true for NNs and other machine learning (ML) methods. In particular, NNs did not perform well in this competition and in subsequent competitions, e.g., more recently, in the NN3 and NN5 forecasting competitions, which were held specifically for ML methods.