Singular Spectrum Analysis — A Hidden Treasure of Time Series Forecasting

Unlock powerful SSA methods to generate highly accurate forecasts.

As a machine learning researcher and data science practitioner, I am always interested to learn and discover new time series forecasting methods early. I Follow myself on LinkedIn and Twitter to get regular updates on some of the most innovative technologies in machine learning and AI, including time series, forecasting and blazing-hot Conformal Prediction and check out my book ‘Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications.’

At NeurIPS2019, an interesting paper, ‘On Multivariate Singular Spectrum Analysis and its Variants’, was published by MIT researchers, prompting me to explore Singular Spectrum Analysis methods for time series forecasting.

It turned out, Singular Spectrum Analysis (SSA) methods have been around for some time, but this powerful technique was less familiar to the mainstream whilst it has been successfully used in meteorology, hydrology, geophysics, climatology, economics, biology, physics, medicine and other sciences.

The Singular Spectrum Analysis (SSA), also known as the ‘Caterpillar’ method for reasons explained below, is a model-free technique for time series analysis. The ideas for the caterpillar methods were independently developed in the USSR and the USA.

Singular Spectrum Analysis (SSA) is a non-parametric technique used to analyse and forecast time series data. It is a powerful method that combines elements from classical time series analysis, multivariate statistics, and signal processing. SSA can decompose a time series into a set of interpretable components, each having a meaningful interpretation.

Singular Spectrum Analysis (SSA) is a rather general time series method used for decomposition, trend extraction, periodicity detection and extraction, signal extraction, denoising, filtering, forecasting, missing data imputation, change point detection, and spectral analysis. The method is model-free and nonparametric, making it well suited for exploratory analysis of time series.

The main steps in Singular Spectrum Analysis (SSA) include embedding, Singular Value…

Valeriy Manokhin, PhD, MBA, CQF

Principal Data Scientist, PhD in Machine Learning, creator of Awesome Conformal Prediction 👍Tip: hold down the Clap icon for up x50

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