mlsanityData to 7 Oct 2026

Feature Programming for Multivariate Time Series Prediction

arXiv 2306.06252 ↗First posted 9 Jun 2023cs.LG stat.MLcited by 7

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Abstract (arXiv)

We introduce the concept of programmable feature engineering for time series modeling and propose a feature programming framework. This framework generates large amounts of predictive features for noisy multivariate time series while allowing users to incorporate their inductive bias with minimal effort. The key motivation of our framework is to view any multivariate time series as a cumulative sum of fine-grained trajectory increments, with each increment governed by a novel spin-gas dynamical Ising model. This fine-grained perspective motivates the development of a parsimonious set of operators that summarize multivariate time series in an abstract fashion, serving as the foundation for large-scale automated feature engineering. Numerically, we validate the efficacy of our method on several synthetic and real-world noisy time series datasets.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 24 of its 74 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

24 of 74 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  3. 2014How transferable are features in deep neural networks?cited by 1,216
  4. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  5. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,062
  6. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  7. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  8. 2014Distributed Representations of Sentences and Documentscited by 677
  9. 2018Deep learning for time series classification: a reviewcited by 575
  10. 2016Domain Separation Networkscited by 317
  11. 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 270
  12. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
12 more
  1. 2020An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networkscited by 128
  2. 2020SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustnesscited by 94
  3. 2017Evolving Deep Neural Networkscited by 93
  4. 2019Probabilistic Forecasting with Temporal Convolutional Neural Networkcited by 63
  5. 2018Can recurrent neural networks warp time?cited by 53
  6. 2020Machine Learning Advances for Time Series Forecastingcited by 49
  7. 2021Trade the Event: Corporate Events Detection for News-Based Event-Driven Tradingcited by 39
  8. 2017Inverse statistical problems: from the inverse Ising problem to data sciencecited by 36
  9. 2016Distributed and parallel time series feature extraction for industrial big data applicationscited by 29
  10. 2013On Graphical Models via Univariate Exponential Family Distributionscited by 26
  11. 2022Review of automated time series forecasting pipelinescited by 17
  12. 2020VEST: Automatic Feature Engineering for Forecastingcited by 14

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 5.

7 papers.

  1. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  2. 2024Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Modelscited by 38
  3. 2024Outlier-Efficient Hopfield Layers for Large Transformer-Based Modelscited by 38
  4. 2023STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Predictioncited by 35
  5. 2024Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspectivecited by 24
  6. 2024Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Studycited by 4
  7. 2024CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solvercited by 3