A New Time Series Forecasting Model Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Temporal Convolutional Network

In this paper, a new hybrid time series forecasting model based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a temporal convolutional network (TCN) (CEEMDAN-TCN) is proposed. The CEEMDAN is used to decompose the time series data and the TCN is used to obtain a good prediction accuracy. The effectiveness of the model is verified in univariate and multivariate time series forecasting tasks. The experimental results indicate that compared with the long short-term memory model and other hybrid models, the proposed CEEMDAN-TCN model shows a better performance in both univariate and multivariate prediction tasks.

Automated summary

In this paper, a new hybrid time series forecasting model based on CEEMDAN and TCN is proposed. The model shows better performance in decomposing time series data and obtaining accurate predictions, as evidenced by experimental results.