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Time Series Forecasting Using LSTM Networks: A Symbolic Approach

arXiv 2003.05672 ↗First posted 12 Mar 2020cs.LG stat.MLcited by 25

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Abstract (arXiv)

Machine learning methods trained on raw numerical time series data exhibit fundamental limitations such as a high sensitivity to the hyper parameters and even to the initialization of random weights. A combination of a recurrent neural network with a dimension-reducing symbolic representation is proposed and applied for the purpose of time series forecasting. It is shown that the symbolic representation can help to alleviate some of the aforementioned problems and, in addition, might allow for faster training without sacrificing the forecast performance.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 6; 2024: 5; 2025: 3; 2026: 2.

25 papers.

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  1. 2021A comparison of LSTM and GRU networks for learning symbolic sequencescited by 24
  2. 2022FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providerscited by 14
  3. 2023Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecastingcited by 13
  4. 2021LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platformscited by 9
  5. 2022An efficient aggregation method for the symbolic representation of temporal datacited by 7
  6. 2022Predicting Hate Intensity of Twitter Conversation Threadscited by 7
  7. 2021Boosted Embeddings for Time Series Forecastingcited by 6
  8. 2021Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial networkcited by 6
  9. 2022Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applicationscited by 4
  10. 2024LLM-ABBA: Understanding time series via symbolic approximationcited by 4
  11. 2024LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecastingcited by 2
  12. 2021Dominant motion identification of multi-particle system using deep learning from videocited by 1
13 more
  1. 2023Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modelingcited by 1
  2. 2025Predictive AI with External Knowledge Infusion: Datasets and Benchmarks for Stock Marketscited by 1
  3. 2026Tuning the burn-in phase in training recurrent neural networks improves their performancecited by 1
  4. 2023ASTRIDE: Adaptive Symbolization for Time Series Databasescited by 0
  5. 2023Forecasting Crude Oil Prices Using Reservoir Computing Modelscited by 0
  6. 2023A Decision Making Framework for Recommended Maintenance of Road Segmentscited by 0
  7. 2023Deep State-Space Model for Predicting Cryptocurrency Pricecited by 0
  8. 2024Tree-based Learning for High-Fidelity Prediction of Chaoscited by 0
  9. 2024Advancing Long-Term Multi-Energy Load Forecasting with Patchformer: A Patch and Transformer-Based Approachcited by 0
  10. 2024QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregationcited by 0
  11. 2025Applied Machine Learning Methods with Long-Short Term Memory Based Recurrent Neural Networks for Multivariate Temperature Predictioncited by 0
  12. 2025Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Modelscited by 0
  13. 2026Multivariate LSTM-Based Forecasting for Renewable Energy: Enhancing Climate Change Mitigationcited by 0