Question

Which forecasting method smoothes out seasonal trends? moving average method decomposition of time series method weighted average method na�ve method

Answer 1

The decomposition of time series method smoothes out seasonal trends.

Step-by-step explanation:

The forecasting method that smoothes out seasonal trends is the decomposition of time series method. This method decomposes a time series into its seasonal, trend, and residual components.

By removing the seasonal component, it provides a smoother forecast that eliminates the influence of seasonal fluctuations.

For example, let's say we have sales data for a retail store that shows higher sales during the holiday season. Using the decomposition of time series method, we can remove the seasonal component and obtain a trend line that represents the underlying sales pattern without the seasonal fluctuations.

Answer 2

The decomposition of time series method smoothes out seasonal trends by separating and analyzing the seasonal component within the data. This method provides clarity on underlying trends by identifying and removing seasonal effects.

Step-by-step explanation:

The forecasting method that smoothes out seasonal trends is known as the decomposition of time series method. This method breaks down a time series into several components: trend, seasonal, cyclical, and irregular components. For seasonal trend smoothing, the method specifically focuses on the seasonal component. It seeks to identify and separate the seasonal factors from the original time series to better understand the underlying trend.

Other methods such as the moving average method and the weighted average method are also used for forecasting, but they do not specifically address seasonality in the same way. The naïve method, on the other hand, is based on the assumption that the most recent observation is the best reflection of the future, which does not take into account seasonality at all.