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An Attention Free Long Short-Term Memory for Time Series Forecasting

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  • Hugo Inzirillo
  • Ludovic De Villelongue

Abstract

Deep learning is playing an increasingly important role in time series analysis. We focused on time series forecasting using attention free mechanism, a more efficient framework, and proposed a new architecture for time series prediction for which linear models seem to be unable to capture the time dependence. We proposed an architecture built using attention free LSTM layers that overcome linear models for conditional variance prediction. Our findings confirm the validity of our model, which also allowed to improve the prediction capacity of a LSTM, while improving the efficiency of the learning task.

Suggested Citation

  • Hugo Inzirillo & Ludovic De Villelongue, 2022. "An Attention Free Long Short-Term Memory for Time Series Forecasting," Papers 2209.09548, arXiv.org.
    • Handle: RePEc:arx:papers:2209.09548
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    2. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    3. Nessrine Hamzaoui & Boutheina Regaieg, 2016. "The Glosten-Jagannathan-Runkle-Generalized Autoregressive Conditional Heteroscedastic approach to investigating the foreign exchange forward premium volatility," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1608-1615.
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