# STD: A Seasonal-Trend-Dispersion Decomposition of Time Series

arXiv: https://arxiv.org/abs/2204.10398 · HTML: https://mlsanity.com/paper/2204.10398/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [STR: Seasonal-Trend Decomposition Using Regression](https://mlsanity.com/paper/2009.05894/) (2020)
- [ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://mlsanity.com/paper/2203.00937/) (2022)
- [Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study](https://mlsanity.com/paper/2003.01475/) (2020)

## Built on by (most cited first, 4 of 4)

- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting](https://mlsanity.com/paper/2212.06620/) (2022)
- [MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting](https://mlsanity.com/paper/2411.17382/) (2024)
- [Deep Time-Series Forecasting in 10 Years: A Survey](https://mlsanity.com/paper/2603.19899/) (2026)

## Abstract (arXiv)

The decomposition of a time series is an essential task that helps to understand its very nature. It facilitates the analysis and forecasting of complex time series expressing various hidden components such as the trend, seasonal components, cyclic components and irregular fluctuations. Therefore, it is crucial in many fields for forecasting and decision processes. In recent years, many methods of time series decomposition have been developed, which extract and reveal different time series properties. Unfortunately, they neglect a very important property, i.e. time series variance. To deal with heteroscedasticity in time series, the method proposed in this work -- a seasonal-trend-dispersion decomposition (STD) -- extracts the trend, seasonal component and component related to the dispersion of the time series. We define STD decomposition in two ways: with and without an irregular component. We show how STD can be used for time series analysis and forecasting.