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📉 Time Series Decomposition
Trend · Seasonal · Random (residual) decomposition · diagnose seasonal period
Tip: enter a chronological series; the tool runs trend/seasonal decomposition (additive or multiplicative) and returns a residual diagnostic (unit-root + seasonal autocorrelation test) to help judge the seasonal period and model form. For forecasting, use the ARIMA/SARIMA tool.
Historical data (chronological order, one value per line)
Decomposition Result
Detailed Table
Notes
· Multiplicative decomposition requires strictly positive data (all ≥ 0 and at least one > 0); the tool internally log-transforms and then handles it additively.
· Seasonality is judged from the residual autocorrelation Q test and seasonal spike: a significant seasonal effect in the residuals (p<0.05) means seasonality exists; otherwise there is no clear seasonality.
· Decomposition is for structural diagnosis only; for forecasting use the ARIMA/SARIMA tool.
About Time Series Decomposition
Time series decomposition tool: split your series into trend, seasonal and residual components (additive or multiplicative), assess seasonality strength and export the decomposed chart.
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