Kasun Bandara, Christoph Bergmeir, Slawek Smyl
arXiv 9 Oct 2017 · Machine Learning · publishedExpert Systems with Applications (2019) · 29 citations (OpenAlex)
arXiv:1710.03222 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hyndman, R. J., Wang, E., Laptev, N., Nov (2015) b | 1.000 | 5 | 3 | 100% |
| 2 | Crone, S. F., Hibon, M., Nikolopoulos, K (2011) Advances in forecasting with neural networks? empirical evidence from the NN3 competition on time series prediction | 0.874 | 5 | 2 | 100% |
| 3 | Hornik, K., Jan (1991) Approximation capabilities of multilayer feedforward networks | 0.843 | 3 | 3 | 100% |
| 4 | Zhang, G., Patuwo, B. E., Hu, M. Y (1998) Forecasting with artificial neural networks:: The state of the art | 0.811 | 4 | 2 | 100% |
| 5 | Ben Taieb, S., Bontempi, G., Atiya, A., Sorjamaa, A (2011) A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition | 0.737 | 3 | 2 | 100% |
| 6 | Rahman, M. M., Islam, M. M., Murase, K., Yao, X (2016) Layered ensemble architecture for time series forecasting | 0.737 | 3 | 2 | 100% |
| 7 | St epni cka, M., Burda, M (2016) Computational intelligence in forecasting (CIF) 2016 time series forecasting competition | 0.737 | 3 | 2 | 100% |
| 8 | Wallace, C. S., Dowe, D. L., Jan (2000) MML clustering of multi-state, poisson, von mises circular and gaussian distributions | 0.737 | 3 | 2 | 100% |
| 9 | Yan, W (2012) Toward automatic time-series forecasting using neural networks | 0.737 | 3 | 2 | 100% |
| 10 | Zimmermann, H.-G., Tietz, C., Grothmann, R (2012) Forecasting with recurrent neural networks: 12 tricks | 0.737 | 3 | 2 | 100% |
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