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MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns

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arxiv 2107.13462 v1 pith:YOCCRHET submitted 2021-07-28 stat.AP stat.CO

Kasun Bandara , Rob J Hyndman , Christoph Bergmeir This is my paper
classification stat.APstat.CO
keywords seriestimedecompositionmstlmultipledatapatternsseasonal
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The decomposition of time series into components is an important task that helps to understand time series and can enable better forecasting. Nowadays, with high sampling rates leading to high-frequency data (such as daily, hourly, or minutely data), many real-world datasets contain time series data that can exhibit multiple seasonal patterns. Although several methods have been proposed to decompose time series better under these circumstances, they are often computationally inefficient or inaccurate. In this study, we propose Multiple Seasonal-Trend decomposition using Loess (MSTL), an extension to the traditional Seasonal-Trend decomposition using Loess (STL) procedure, allowing the decomposition of time series with multiple seasonal patterns. In our evaluation on synthetic and a perturbed real-world time series dataset, compared to other decomposition benchmarks, MSTL demonstrates competitive results with lower computational cost. The implementation of MSTL is available in the R package forecast.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting

    cs.LG 2025-09 conditional novelty 6.0 of 10

    ARIES shows that deep forecasting models have consistent performance preferences tied to time series properties, and uses those preferences to recommend models for new datasets.

  2. BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A balanced sampling strategy over statistically characterized time series patterns lets universal forecasting models train on 78 billion tokens instead of 419 billion, with equal or better zero-shot accuracy.

  3. A Stability-Driven Framework for Long-Term Hourly Electricity Demand Forecasting

    stat.ME 2025-07 conditional novelty 5.0 of 10

    A GDP-based annual forecast combined with stable seasonality indices from the baseline year yields 4-7% MAPE for multi-year-ahead hourly electricity load in Singapore, Belgium, and Bulgaria.

  4. Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

    cs.LG 2025-06 conditional novelty 5.0 of 10

    No time series foundation model statistically outperforms the biseasonal MSTL model in most European day-ahead electricity price markets in 2024, though Chronos-Bolt and Time-MoE match traditional methods.

  5. LGTD: Local-Global Trend Decomposition for Season-Length-Free Time Series Analysis

    cs.DB 2026-01 reject novelty 4.0 of 10

    LGTD decomposes time series into global trend, error-driven piecewise-linear local trends (treated as emergent seasonality), and residuals, requiring no season-length input.

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