# Unlocking the Power of LSTM for Long Term Time Series Forecasting

arXiv: https://arxiv.org/abs/2408.10006 · HTML: https://mlsanity.com/paper/2408.10006/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs](https://mlsanity.com/paper/1612.05231/) (2016)
- [Do RNN and LSTM have Long Memory?](https://mlsanity.com/paper/2006.03860/) (2020)
- [CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables](https://mlsanity.com/paper/2403.01673/) (2024)
- [ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting](https://mlsanity.com/paper/2308.07496/) (2023)
- [Learning with Interpretable Structure from Gated RNN](https://mlsanity.com/paper/1810.10708/) (2018)
- [Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer](https://mlsanity.com/paper/2408.03320/) (2024)

## Built on by (most cited first, 29 of 29)

- [AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery](https://mlsanity.com/paper/2604.05550/) (2026)
- [Time-o1: Time-Series Forecasting Needs Transformed Label Alignment](https://mlsanity.com/paper/2505.17847/) (2025)
- [xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories](https://mlsanity.com/paper/2410.16928/) (2024)
- [Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels](https://mlsanity.com/paper/2503.14376/) (2025)
- [AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset](https://mlsanity.com/paper/2502.09919/) (2025)
- [BenchECG and xECG: a benchmark and baseline for ECG foundation models](https://mlsanity.com/paper/2509.10151/) (2025)
- [TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness](https://mlsanity.com/paper/2506.06482/) (2025)
- [TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning](https://mlsanity.com/paper/2606.01498/) (2026)
- [HiPPO-KAN: Efficient KAN Model for Time Series Analysis](https://mlsanity.com/paper/2410.14939/) (2024)
- [Semantic-Enhanced Time-Series Forecasting via Large Language Models](https://mlsanity.com/paper/2508.07697/) (2025)
- [Deep Time-series Forecasting Needs Kernelized Moment Balancing](https://mlsanity.com/paper/2602.00717/) (2026)
- [Predictive AI with External Knowledge Infusion: Datasets and Benchmarks for Stock Markets](https://mlsanity.com/paper/2504.20058/) (2025)
- [How Effective Are Time-Series Models for Precipitation Nowcasting? A Comprehensive Benchmark for GNSS-based Precipitation Nowcasting](https://mlsanity.com/paper/2509.25263/) (2025)
- [TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting](https://mlsanity.com/paper/2510.06680/) (2025)
- [Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions](https://mlsanity.com/paper/2507.19639/) (2025)
- [Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series](https://mlsanity.com/paper/2507.21531/) (2025)
- [Filter then Attend: Improving attention-based Time Series Forecasting with Spectral Filtering](https://mlsanity.com/paper/2508.20206/) (2025)
- [StoxLSTM: A Stochastic Extended Long Short-Term Memory Network for Time Series Forecasting](https://mlsanity.com/paper/2509.01187/) (2025)
- [An overview of neural architectures for self-supervised audio representation learning from masked spectrograms](https://mlsanity.com/paper/2509.18691/) (2025)
- [AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention](https://mlsanity.com/paper/2510.00049/) (2025)
- [Rethinking Recurrent Neural Networks for Time Series Forecasting: A Reinforced Recurrent Encoder with Prediction-Oriented Proximal Policy Optimization](https://mlsanity.com/paper/2601.03683/) (2026)
- [We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification](https://mlsanity.com/paper/2601.10312/) (2026)
- [Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance](https://mlsanity.com/paper/2603.01820/) (2026)
- [Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction](https://mlsanity.com/paper/2603.03531/) (2026)
- [Deep Time-Series Forecasting in 10 Years: A Survey](https://mlsanity.com/paper/2603.19899/) (2026)
- [PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting](https://mlsanity.com/paper/2605.21550/) (2026)
- [CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction](https://mlsanity.com/paper/2606.00338/) (2026)
- [Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System](https://mlsanity.com/paper/2608.01648/) (2026)
- [Mix&Fix-Net: A Dual-Stage Trajectory Prediction Model for AIS and Vision-Derived Vessel Data](https://mlsanity.com/paper/2608.19580/) (2026)

## Abstract (arXiv)

Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF) tasks. While the recently introduced sLSTM for Natural Language Processing (NLP) introduces exponential gating and memory mixing that are beneficial for long term sequential learning, its potential short memory issue is a barrier to applying sLSTM directly in TSF. To address this, we propose a simple yet efficient algorithm named P-sLSTM, which is built upon sLSTM by incorporating patching and channel independence. These modifications substantially enhance sLSTM's performance in TSF, achieving state-of-the-art results. Furthermore, we provide theoretical justifications for our design, and conduct extensive comparative and analytical experiments to fully validate the efficiency and superior performance of our model.