Title: Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach,
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 methods in this space, a notion of similarity
between the time series needs to be built into the methods. To this end, we
present a prediction model that can be used with different types of RNN models
on subgroups of similar time series, which are identified by time series
clustering techniques. We assess our proposed methodology using LSTM networks,
a widely popular RNN variant. Our method achieves competitive results on
benchmarking datasets under competition evaluation procedures. In particular,
in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM
model and outperforms all other methods on the CIF2016 forecasting competition
dataset.