Differential Machine Learning for Time Series Prediction

• URL: http://arxiv.org/abs/2503.03302v2
• Date: Sun, 09 Mar 2025 02:42:26 GMT
• Title: Differential Machine Learning for Time Series Prediction
• Authors: Akash Yadav, Eulalia Nualart,
• Abstract summary: We propose a novel approach that enhances neural network predictions through differential learning.<n>We develop a differential long short-term memory (Diff-LSTM) network that uses a shared LSTM cell to simultaneously process both data streams.
• Score: 1.3812010983144802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate time series prediction is challenging due to the inherent nonlinearity and sensitivity to initial conditions. We propose a novel approach that enhances neural network predictions through differential learning, which involves training models on both the original time series and its differential series. Specifically, we develop a differential long short-term memory (Diff-LSTM) network that uses a shared LSTM cell to simultaneously process both data streams, effectively capturing intrinsic patterns and temporal dynamics. Evaluated on the Mackey-Glass, Lorenz, and R\"ossler chaotic time series, as well as a real-world financial dataset from ACI Worldwide Inc., our results demonstrate that the Diff- LSTM network outperforms prevalent models such as recurrent neural networks, convolutional neural networks, and bidirectional and encoder-decoder LSTM networks in both short-term and long-term predictions. This framework offers a promising solution for enhancing time series prediction, even when comprehensive knowledge of the underlying dynamics of the time series is not fully available.

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