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

By Kasun Bandara, Christoph Bergmeir, Slawek Smyl. Expert systems with applications, 2017

428 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/44e92d9dc0fdc6ca5b5942c1c24a3cfedab8d1b3

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Citations in the corpus

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, methodology): “As suggested in recent forecasting studies that use neural networks [33,34], in this work, we adopt the MIMO strategy (Multi-Input Multi-Output) which belongs to the last category.”
Forecasting: theory and practice (2020, background): “Recently, to deal with these new challenges, numerous empirical studies have identified the potentials of deep learning based global models, in both point and probabilistic demand forecasting (e.g., Wen et al., 2017; Rangapuram et al., 2018; Salinas et al., 2019a; Bandara et al., 2020b).”
A survey on sentiment analysis methods, applications, and challenges (2022, methodology): “Variants of RNN (Pham and Le-Hong 2017) like LSTM (Bandara et al. 2020), GRU (Cheng et al. 2020), Bi-LSTM (Abid et al. 2019; Cho and Lee 2019) have been used extensively in Sentiment analysis and related NLP task (Abid et al. 2019; Khan et al. 2016).”
Probabilistic Forecasting with Temporal Convolutional Neural Network (2019, background): “Both RNNs and CNNs have been shown to be able to model complex nonlinear feature interactions and yield substantial forecasting performances, especially when many related time series are available [2, 9, 18, 27, 29, 34].”
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019, methodology): “, 2014; Bayer and Osendorfer, 2014; Schuster and Paliwal, 1997). Nevertheless, these latter RNN units have hardly been used in the forecasting literature. They were mostly designed with language modelling tasks in mind. Jozefowicz et al. (2015) perform an empirical evaluation of different RNN cells.”