Neural Networks Fusion for Enhanced Time Series Forecasting with Missing Data

Achieving high accuracy in time series forecasting, especially with missing data, is crucial. This paper proposes a novel neural networks approach, in which we use multiple time series datasets, potentially pre-processed using techniques like ARIMA, to capture temporal dependencies. Neural networks are then employed for two key functionalities: (1) data fusion to capture complex relationships between datasets, and (2) robust missing data handling through model training. This method addresses the challenges of incomplete wind farm data and aims to significantly improve forecasting accuracy. It is applied on several wind power datasets for experiments, the results show effectiveness of the proposed model.