# Towards Better Forecasting by Fusing Near and Distant Future Visions

arXiv: https://arxiv.org/abs/1912.05122 · HTML: https://mlsanity.com/paper/1912.05122/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 14 of 14)

- [SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2308.11200/) (2023)
- [Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting](https://mlsanity.com/paper/2101.10460/) (2021)
- [RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks](https://mlsanity.com/paper/2401.09093/) (2024)
- [A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting](https://mlsanity.com/paper/2109.01657/) (2021)
- [Multi-scale Attention Flow for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2205.07493/) (2022)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Towards Better Long-range Time Series Forecasting using Generative Forecasting](https://mlsanity.com/paper/2212.06142/) (2022)
- [Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism](https://mlsanity.com/paper/2506.15688/) (2025)
- [EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables](https://mlsanity.com/paper/2208.01913/) (2022)
- [Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks](https://mlsanity.com/paper/2110.08770/) (2021)
- [KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting](https://mlsanity.com/paper/2202.03903/) (2022)
- [From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics*](https://mlsanity.com/paper/2206.01176/) (2022)
- [Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network](https://mlsanity.com/paper/2303.16532/) (2023)
- [Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training](https://mlsanity.com/paper/2304.11043/) (2023)

## Abstract (arXiv)

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.