mlsanityData to 14 Oct 2026

Conditional Time Series Forecasting with Convolutional Neural Networks

arXiv 1703.04691 ↗First posted 14 Mar 2017stat.MLcited by 123

Not on the map. Why?

Abstract (arXiv)

We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. We test and analyze the performance of the convolutional network both unconditionally as well as conditionally for financial time series forecasting using the S&P500, the volatility index, the CBOE interest rate and several exchange rates and extensively compare it to the performance of the well-known autoregressive model and a long-short term memory network. We show that a convolutional network is well-suited for regression-type problems and is able to effectively learn dependencies in and between the series without the need for long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

Key facts. Cited by 123 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Mar 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 25; 2022: 15; 2023: 33; 2024: 18; 2025: 22; 2026: 10.

123 papers.

Sort:

By area:

  1. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  2. 2023A decoder-only foundation model for time-series forecastingcited by 692
  3. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  4. 2021Monash Time Series Forecasting Archivecited by 267
  5. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  6. 2024Kolmogorov-Arnold Networks (KANs) for Time Series Analysiscited by 98
  7. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 90
  8. 2021Domain Adaptation for Time Series Forecasting via Attention Sharingcited by 68
  9. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  10. 2021Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecastingcited by 32
  11. 2023Mixture-of-Linear-Experts for Long-term Time Series Forecastingcited by 29
  12. 2024CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variablescited by 25
38 more
  1. 2022Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecastingcited by 23
  2. 2025Foundation Models for Time Series: A Surveycited by 23
  3. 2021Long Horizon Forecasting With Temporal Point Processescited by 20
  4. 2022Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Trackingcited by 18
  5. 2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsamplingcited by 17
  6. 2023Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distributioncited by 15
  7. 2022Efficient Automated Deep Learning for Time Series Forecastingcited by 14
  8. 2024xLSTMTime : Long-term Time Series Forecasting With xLSTMcited by 14
  9. 2021Corn Yield Prediction with Ensemble CNN-DNNcited by 12
  10. 2024Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Predictioncited by 11
  11. 2025Diffusion Models for Time Series Forecasting: A Surveycited by 11
  12. 2025Predictive inference for time series: why is split conformal effective despite temporal dependence?cited by 10
  13. 2021Hierarchically Regularized Deep Forecastingcited by 9
  14. 2024A Mamba Foundation Model for Time Series Forecastingcited by 9
  15. 2025Multimodal Conditioned Diffusive Time Series Forecastingcited by 9
  16. 2023Targeted Attacks on Timeseries Forecastingcited by 8
  17. 2023Bayesian neural networks via MCMC: a Python-based tutorialcited by 8
  18. 2021Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLcited by 7
  19. 2022Predicting Hate Intensity of Twitter Conversation Threadscited by 7
  20. 2023TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecastingcited by 7
  21. 2021Conditional Generation of Periodic Signals with Fourier-Based Decodercited by 6
  22. 2022DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutionscited by 6
  23. 2023Deep Learning Models for Flood Predictions in South Floridacited by 6
  24. 2023ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learningcited by 6
  25. 2023AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecastingcited by 6
  26. 2024Deep Coupling Network For Multivariate Time Series Forecastingcited by 6
  27. 2025Over-squashing in Spatiotemporal Graph Neural Networkscited by 6
  28. 2021Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approachcited by 5
  29. 2023Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problemcited by 5
  30. 2023Differential Convolutional Fuzzy Time Series Forecastingcited by 5
  31. 2023A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecastingcited by 5
  32. 2023Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?cited by 5
  33. 2024Structural Knowledge Informed Continual Multivariate Time Series Forecastingcited by 5
  34. 2025Retrieval Augmented Time Series Forecastingcited by 5
  35. 2021Time Series (re)sampling using Generative Adversarial Networkscited by 4
  36. 2021Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directionscited by 4
  37. 2025General Information Metrics for Improving AI Model Training Efficiencycited by 4
  38. 2021Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learningcited by 3

50 most cited of 123 All 123 (JSON)