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What are the assumptions of stl decomposition?

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The Seasonal-Trend decomposition by Loess (STL) method makes several assumptions when decomposing time series data. Firstly, STL assumes that the time series can be decomposed into three components: seasonal, trend, and remainder components . Secondly, it assumes that the decomposition process can effectively split the original time series into these distinct components, allowing for better analysis and forecasting . Additionally, STL assumes that the decomposition results can be utilized in combination with various forecasting methods, such as statistical methods like Theta and machine learning methods like LSTM, to improve forecasting accuracy . Lastly, STL assumes that the decomposed components can be effectively forecasted individually and then aggregated to provide a comprehensive forecast for the original time series data .

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Open access•Posted Content•DOI STL decomposition based LSTM model for seasonal agricultural price forecasting 22 Aug 2022	Not addressed in the paper.
Patent Symbolized directed weighted complex network construction method based on an STL decomposition method Li-Na Wang, Cheng Yuanyuan, Chen-Rui Zang, Shen Jianglong, Gui-Min Tan - Show less +4 more 17 May 2019	Not addressed in the paper.
Book Chapter•DOI Improving Forecasting by Resampling STL Decomposition Clara Cordeiro, M. Rosário Ramos, M. Manuela Neves - Show less +2 more 01 Jan 2023-Lecture Notes in Computer Science	Not addressed in the paper.
Open access•Journal Article•DOI Stock Price Prediction through STL Decomposition using Multivariate Two-way Long Short-term Memory Junsuke Senoguchi 09 Oct 2022-Journal of computer science and technology studies	Assumptions of STL decomposition include additive time-series data representation, with components for trend, seasonal, and residual factors, as utilized in stock price prediction models in the study.
Open access•Journal Article•DOI STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones Zuokun Ouyang, Philippe Ravier, Meryem Jabloun - Show less +2 more 08 Jul 2021 15 Citations	Not addressed in the paper.

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