FAEP claims state-of-the-art forecasts of New South Wales electricity price volatility by adding LLM-generated weather features to a hybrid XGBoost-LSTM model.
MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural Network
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abstract
Time series analysis and modelling constitute a crucial research area. Traditional artificial neural networks struggle with complex, non-stationary time series data due to high computational complexity, limited ability to capture temporal information, and difficulty in handling event-driven data. To address these challenges, we propose a Multi-modal Time Series Analysis Model Based on Spiking Neural Network (MTSA-SNN). The Pulse Encoder unifies the encoding of temporal images and sequential information in a common pulse-based representation. The Joint Learning Module employs a joint learning function and weight allocation mechanism to fuse information from multi-modal pulse signals complementary. Additionally, we incorporate wavelet transform operations to enhance the model's ability to analyze and evaluate temporal information. Experimental results demonstrate that our method achieved superior performance on three complex time-series tasks. This work provides an effective event-driven approach to overcome the challenges associated with analyzing intricate temporal information. Access to the source code is available at https://github.com/Chenngzz/MTSA-SNN}{https://github.com/Chenngzz/MTSA-SNN
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cs.CE 1years
2025 1verdicts
REJECT 1representative citing papers
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LLM-Enhanced Feature Engineering for Multi-Factor Electricity Price Predictions
FAEP claims state-of-the-art forecasts of New South Wales electricity price volatility by adding LLM-generated weather features to a hybrid XGBoost-LSTM model.