Interval Prediction for Multivariate Long-Term Time Series Electricity Consumption Based on Dual-Channel Modeling

Résumé fourni par la source

Prediction interval models have been proposed to characterize uncertain phenomena, which provide expected values and the upper and lower bounds of the interval given a desired coverage probability. However, existing methods of generating prediction intervals mainly use simple models such as recurrent neural network (RNN), long short-term memory (LSTM), or predominant models such as transformers as the backbone network, which have limitations in modeling long-term multivariate sequences. To address the issue, the paper proposes LMDCPI (long-term multivariate dual channel prediction interval), which employs dual branches to capture both intra-variable temporal dependencies and inter-variable dependencies from global and local perspectives. Experimental results show that LMDCPI achieves state-of-the-art performance on several public data sets.

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