  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. Inspired by
the Construal Level Theory of psychology, this model aims to improve the
predictive performance by fusing forecasting information (i.e., future visions)
of different future time. We first use the Convolution Neural Network to
extract multi-level abstract representations of the raw data for near and
distant future predictions. We then model the interplay between multiple
predictive tasks and fuse their future visions through a modified
Encoder-Decoder architecture. Finally, we combine traditional Autoregression
model with the neural network to solve the scale insensitive problem.
Experiments on three real-world datasets show that our method achieves
statistically significant improvements compared to the most state-of-the-art
baseline methods, with average 4.59% reduction on RMSE metric and average 6.87%
reduction on MAE metric.
