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seasonal_decompose

Time series decomposition helps analyze patterns in time series data. It breaks down data into : - trend, The general direction in which the data moves for a long period; - seasonal The repeating short-term cycle or pattern - residual The random noise that cannot be attributed to trend or seasonality

Types

Additive Decomposition

The time series is represented as the sum of its components;

Multiplicative Decomposition

The time series is represented as the product of its components;

Methods

Moving Averages

Classical Decomposition

STL: Seasonal and Trend Decomposition using Loess

ETS: Exponential Smoothing State Space Model