# Recurrent Neural Networks for Time Series Forecasting

arXiv: https://arxiv.org/abs/1901.00069 · HTML: https://mlsanity.com/paper/1901.00069/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2019.

## Built on by (most cited first, 19 of 19)

- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices](https://mlsanity.com/paper/2203.10716/) (2022)
- [ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis](https://mlsanity.com/paper/2403.01493/) (2024)
- [TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state](https://mlsanity.com/paper/2505.20774/) (2025)
- [Generative Pretrained Hierarchical Transformer for Time Series Forecasting](https://mlsanity.com/paper/2402.16516/) (2024)
- [xLSTMTime : Long-term Time Series Forecasting With xLSTM](https://mlsanity.com/paper/2407.10240/) (2024)
- [Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting](https://mlsanity.com/paper/2602.17634/) (2026)
- [Continuous-Depth Neural Models for Dynamic Graph Prediction](https://mlsanity.com/paper/2106.11581/) (2021)
- [Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data](https://mlsanity.com/paper/2210.12853/) (2022)
- [Mlinear: Rethink the Linear Model for Time-series Forecasting](https://mlsanity.com/paper/2305.04800/) (2023)
- [DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2410.22981/) (2024)
- [DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting](https://mlsanity.com/paper/2601.21726/) (2026)
- [Channel-wise Retrieval for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2604.05543/) (2026)
- [Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network](https://mlsanity.com/paper/2111.04474/) (2021)
- [Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation](https://mlsanity.com/paper/2410.01847/) (2024)
- [A Comprehensive Review on Summarizing Financial News Using Deep Learning](https://mlsanity.com/paper/2109.10118/) (2021)
- [Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics](https://mlsanity.com/paper/2204.10308/) (2022)
- [Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture](https://mlsanity.com/paper/2501.10454/) (2025)
- [Exploring the Role of Artificial Intelligence and Machine Learning in Process Optimization for Chemical Industry](https://mlsanity.com/paper/2502.15768/) (2025)

## Abstract (arXiv)

Time series forecasting is difficult. It is difficult even for recurrent neural networks with their inherent ability to learn sequentiality. This article presents a recurrent neural network based time series forecasting framework covering feature engineering, feature importances, point and interval predictions, and forecast evaluation. The description of the method is followed by an empirical study using both LSTM and GRU networks.