Towards Better Forecasting by Fusing Near and Distant Future Visions

Understand

Multivariate time series forecasting is an important yet challenging problem in machine learning.

• Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments with different temporal distance.
• Such a deficiency probably prevents the model from getting enough information about the future, thus limiting the forecasting accuracy.
• To address this problem, we propose Multi-Level Construal Neural Network (MLCNN), a novel multi-task deep learning framework.