Decomposition of Time Series into Trend, Seasonality & Residual from Scratch
Time series decomposition is a mathematical procedure which transforms a time series into multiple different time series. The original time series is often split into 3 component series:
Seasonal: Patterns that repeat with a fixed period of time. For example, a website might receive more visits during weekends; this would produce data with a seasonality of 7 days.
Trend: The underlying trend of the metrics. A website increasing in popularity should show a general trend that goes up.
Random: Also call “noise”, “irregular” or “remainder”, this is the residuals of the original time series after the seasonal & trend series are removed.
In this video, you will discover time series decomposition from scratch.
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