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Optimization of LSTM Algorithm Through Outliers – Application to Financial Time Series Forecasting

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1207)

Abstract

The long short-term memory (LSTM) model is widely used in multiple areas, mainly for speech recognition, natural language processing and activity recognition. In the last few years, we started to see many variants of LSTM for recurrent neural networks since its inception in 1997. However, there weren’t many studies that have addressed the LSTM’s gating mechanism. In this paper, we propose a novel LSTM framework where we modify the architecture of the LSTM unit by adding a new layer that we call the “outlier gate”. The latter controls the flow of information that goes into the LSTM cell. This added signal allows us to avoid both the carry-over effect that the outliers have on the forecasted point and a bias in the estimates of our LSTM model – caused by unusual or non-repetitive events. The proposed architecture led us to an end-to-end trainable model that we applied in this paper to a financial time-series forecasting problem. Our results demonstrate that the new proposed LSTM architecture achieves better performance than the state-of-the-art original LSTM model.

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Correspondence to Houda Benkerroum .

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Benkerroum, H., Cherif, W., Kissi, M. (2020). Optimization of LSTM Algorithm Through Outliers – Application to Financial Time Series Forecasting. In: Hamlich, M., Bellatreche, L., Mondal, A., Ordonez, C. (eds) Smart Applications and Data Analysis. SADASC 2020. Communications in Computer and Information Science, vol 1207. Springer, Cham. https://doi.org/10.1007/978-3-030-45183-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-45183-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45182-0

  • Online ISBN: 978-3-030-45183-7

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