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Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost

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  • Theodoros Zafeiriou
  • Dimitris Kalles

Abstract

Our study focuses on comparing the performance and resource requirements between different Long Short-Term Memory (LSTM) neural network architectures and an ANN specialized architecture for forex market prediction. We analyze the execution time of the models as well as the resources consumed, such as memory and computational power. Our aim is to demonstrate that the specialized architecture not only achieves better results in forex market prediction but also executes using fewer resources and in a shorter time frame compared to LSTM architectures. This comparative analysis will provide significant insights into the suitability of these two types of architectures for time series prediction in the forex market environment.

Suggested Citation

  • Theodoros Zafeiriou & Dimitris Kalles, 2024. "Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost," Papers 2405.10679, arXiv.org.
    • Handle: RePEc:arx:papers:2405.10679
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    References listed on IDEAS

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    1. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    2. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
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