Seasonality Index Calculator
Calculate seasonal indices and centered moving averages from time-series data. Forecast trends and identify seasonal patterns for inventory and sales analysis.
Enter at least 24 numeric values separated by commas.
| Month | Avg Index | Interpretation |
|---|
About
Time-series analysis relies heavily on isolating seasonal variations from underlying trends. Retailers, supply chain managers, and financial analysts use seasonality indices to smooth data and predict future peaks. Ignoring these cyclic patterns often leads to stockouts during high demand or excess inventory during off-seasons. This tool decomposes raw data using the Ratio-to-Moving-Average method.
The process involves calculating a Centered Moving Average (CMA) to strip away noise and seasonality. By comparing actual values to this smoothed baseline, we derive specific indices for each period. A value above 1.0 indicates a high season, while a value below 1.0 suggests a slowdown. Accurate decomposition is mathematically rigorous but essential for precise forecasting.
Formulas
The calculation utilizes a Centered Moving Average (CMA) to smooth the time series. For a 12-month cycle, the CMA at time t is derived from the average of two consecutive 12-month moving averages.
The Seasonal Index (SI) for a specific month is the average of the ratios of Actual to CMA for that month across all available years.
Reference Data
| Period | Raw Data | MA (12-Period) | Centered MA | Seas. Irreg. | Seas. Index |
|---|---|---|---|---|---|
| Month 1 | 120 | NULL | NULL | NULL | 0.85 |
| Month 2 | 135 | NULL | NULL | NULL | 0.92 |
| Month 7 | 200 | 150.4 | 151.2 | 1.32 | 1.30 |
| Month 8 | 110 | 152.1 | 153.0 | 0.72 | 0.75 |