Journal article

Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

Kasun Bandara, Christoph Bergmeir, Slawek Smyl

Expert Systems with Applications | PERGAMON-ELSEVIER SCIENCE LTD | Published : 2020

Abstract

With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks (RNNs), and in particular Long Short Term Memory (LSTM) networks, have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context, when trained across all available time series. However, if the time series database is heterogeneous, accuracy may degenerate, so that on the way towards fully automatic forecasting m..

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