mlsanityData to 7 Oct 2026

Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis

arXiv 2411.04554 ↗First posted 7 Nov 2024cs.LGcited by 5

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Abstract (arXiv)

Time series analysis finds wide applications in fields such as weather forecasting, anomaly detection, and behavior recognition. Previous methods attempted to model temporal variations directly using 1D time series. However, this has been quite challenging due to the discrete nature of data points in time series and the complexity of periodic variation. In terms of periodicity, taking weather and traffic data as an example, there are multi-periodic variations such as yearly, monthly, weekly, and daily, etc. In order to break through the limitations of the previous methods, we decouple the implied complex periodic variations into inclusion and overlap relationships among different level periodic components based on the observation of the multi-periodicity therein and its inclusion relationships. This explicitly represents the naturally occurring pyramid-like properties in time series, where the top level is the original time series and lower levels consist of periodic components with gradually shorter periods, which we call the periodic pyramid. To further extract complex temporal variations, we introduce self-attention mechanism into the periodic pyramid, capturing complex periodic relationships by computing attention between periodic components based on their inclusion, overlap, and adjacency relationships. Our proposed Peri-midFormer demonstrates outstanding performance in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 31 of its 46 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

31 of 46 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  4. 2020Reformer: The Efficient Transformercited by 1,658
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  7. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  9. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  10. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  11. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  12. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
19 more
  1. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  2. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  3. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  4. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  5. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  6. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 336
  7. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  8. 2018The UEA multivariate time series classification archive, 2018cited by 330
  9. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 267
  10. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  11. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 158
  12. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  13. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
  14. 2024TSLANet: Rethinking Transformers for Time Series Representation Learningcited by 71
  15. 2022Flowformer: Linearizing Transformers with Conservation Flowscited by 69
  16. 2023Causal Recurrent Variational Autoencoder for Medical Time Series Generationcited by 44
  17. 2023BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basiscited by 41
  18. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  19. 2023Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecastingcited by 19

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Byte Pair Encoding for Efficient Time Series Forecastingcited by 5
  2. 2025Multi-Order Wavelet Derivative Transform for Deep Time Series Forecastingcited by 4
  3. 2025Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generationcited by 1
  4. 2026Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecastingcited by 0
  5. 2026No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detectioncited by 0