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where $h_m^+=\lfloor(h-1)\mod~m\rfloor+1$, which ensures that the estimates of the seasonal indices used for forecasting come from the final year of the sample. (The notation $\lfloor u \rfloor$ means the largest integer not greater than $u$.) The level equation shows a weighted average between the seasonally adjusted observation $(y_{t} - s_{t-m})$ and the non-seasonal forecast $(\ell_{t-1}+b_{t-1})$ for time $t$. The trend equation is identical to Holt’s linear method. The seasonal equation shows a weighted average between the current seasonal index, $(y_{t}-\ell_{t-1}-b_{t-1})$, and the seasonal index of the same season last year (., $m$ time periods ago).

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