mlsanityData to 7 Oct 2026

Deep Learning for Time-Series Analysis

arXiv 1701.01887 ↗First posted 7 Jan 2017cs.LGcited by 55

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Abstract (arXiv)

In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jan 2017.

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Citing papers per year: 2021: 13; 2022: 13; 2023: 13; 2024: 6; 2025: 7; 2026: 3.

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  1. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
  2. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  3. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  4. 2021An Experimental Review on Deep Learning Architectures for Time Series Forecastingcited by 47
  5. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  6. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  7. 2021Broad-UNet: Multi-scale feature learning for nowcasting taskscited by 25
  8. 2021Adjusting for Autocorrelated Errors in Neural Networks for Time Seriescited by 18
  9. 2022Rapid training of quantum recurrent neural networkscited by 13
  10. 2022Retrieval Based Time Series Forecastingcited by 13
  11. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  12. 2022Backdoor Attacks on Time Series: A Generative Approachcited by 10
38 more
  1. 2023Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Surveycited by 9
  2. 2021A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalescited by 6
  3. 2022Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognitioncited by 6
  4. 2022StyleTime: Style Transfer for Synthetic Time Series Generationcited by 5
  5. 2021Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones?cited by 3
  6. 2021Multi Scale Graph Wavenet for Wind Speed Forecastingcited by 3
  7. 2022On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamicscited by 3
  8. 2022Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learningcited by 3
  9. 2024GARCH-Informed Neural Networks for Volatility Prediction in Financial Marketscited by 3
  10. 2025Deep-Bench: Deep Learning Benchmark Dataset for Code Generationcited by 3
  11. 2021Challenges and approaches to time-series forecasting in data center telemetry: A Surveycited by 2
  12. 2022FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysiscited by 2
  13. 2022Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecastingcited by 2
  14. 2023Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Modelcited by 2
  15. 2025Large Language models for Time Series Analysis: Techniques, Applications, and Challengescited by 2
  16. 2021PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classificationcited by 1
  17. 2021DTWSSE: Data Augmentation with a Siamese Encoder for Time Seriescited by 1
  18. 2022Deep Learning for Prawn Farming: Forecasting and Anomaly Detectioncited by 1
  19. 2023Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecastingcited by 1
  20. 2023Control of synaptic plasticity in neural networkscited by 1
  21. 2023Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformerscited by 1
  22. 2023Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Predictioncited by 1
  23. 2024Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Predictioncited by 1
  24. 2025HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasetscited by 1
  25. 2025TinyD'ej`aVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streamscited by 1
  26. 2026Regime-aware financial volatility forecasting via in-context learningcited by 1
  27. 2021Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Datacited by 0
  28. 2021Convolutional Neural Network(CNN/ConvNet) in Stock Price Movement Predictioncited by 0
  29. 2021MegazordNet: combining statistical and machine learning standpoints for time series forecastingcited by 0
  30. 2022Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time seriescited by 0
  31. 2022Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populationscited by 0
  32. 2022Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Frameworkcited by 0
  33. 2023Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Seriescited by 0
  34. 2023Judging a video by its bitstream covercited by 0
  35. 2023Nowcasting day-ahead marginal emissions using multi-headed CNNs and deep generative modelscited by 0
  36. 2024Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attackscited by 0
  37. 2024An Active Learning Framework with a Class Balancing Strategy for Time Series Classificationcited by 0
  38. 2024Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Surveycited by 0

50 most cited of 55 All 55 (JSON)