<P> y ^ T + h T = y T + h − k m (\ displaystyle (\ hat (y)) _ (T + h T) = y_ (T + h - km)) where m (\ displaystyle m) = seasonal period and k (\ displaystyle k) is the smallest integer greater than (h − 1) / m (\ displaystyle (h - 1) / m). </P> <P> The seasonal naïve method is particularly useful for data that has a very high level of seasonality . </P> <P> Time series methods use historical data as the basis of estimating future outcomes . </P> <Ul> <Li> Moving average </Li> <Li> Weighted moving average </Li> <Li> Kalman filtering </Li> <Li> Exponential smoothing </Li> <Li> Autoregressive moving average (ARMA) </Li> <Li> Autoregressive integrated moving average (ARIMA) </Li> </Ul>

Which of the following basic patterns of demand is difficult to predict