<ul><li>Time series decomposition is based on the idea that time series can be broken down into four fundamental components: trend, seasonality, cyclical patterns, and random variations.</li><li>Classical decomposition provides valuable insights, but modern techniques like the STL method offer more flexibility and robustness in handling seasonal components.</li><li>Decomposition results provide actionable insights about trend direction, trend strength, seasonal amplitude, and residual variance.</li><li>Seasonal and trend decomposition methods are essential for understanding time series data and can inform forecasting, anomaly detection, and pattern analysis.</li></ul>

Seasonal and Trend Decomposition Methods for Time Series Forecasting

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