Differential Machine Learning for Time Series Prediction

2025-03-05

AI Summary

Time-series forecasting faces accuracy bottlenecks due to strong nonlinearity and sensitivity to initial conditions. To address this, we propose a differential machine learning paradigm that jointly models the original time series and its first-order difference sequence, thereby enhancing representation of dynamic evolution patterns. We introduce Diff-LSTM—a novel architecture featuring shared-weight LSTM units that process both sequences synchronously—requiring no prior dynamical knowledge while improving robustness and generalization. The model is jointly trained and evaluated on chaotic systems (Mackey–Glass, Lorenz, Rössler) and real-world financial time series. Experiments demonstrate consistent superiority over RNNs, CNNs, BiLSTMs, and Encoder–Decoder LSTMs across short- and long-term forecasting tasks, achieving significantly lower average prediction errors on both chaotic and financial time series.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Enhances time series prediction accuracy using differential learning.
• Develops a Diff-LSTM network for capturing temporal dynamics.
• Outperforms existing models in chaotic and financial datasets.

Innovation

• Differential learning enhances neural network predictions.
• Diff-LSTM processes original and differential series simultaneously.
• Diff-LSTM outperforms RNN, CNN, and other LSTM variants.