Contents
What does seasonal adjustment do to a time series?
Seasonal adjustment is the estimation and removal of seasonal effects from a time series to reveal certain nonseasonal features. Seasonal effects are the persistent, repeated effects that occur at the same time each year, and that are not explainable by the dynamics of trends or cycles.
How does the calendar affect the time series?
Calendar effects (trading days and holidays) often introduce additional movement in the time-series, and data outliers may disrupt movement altogether. Both calendar effects and data outliers make it difficult to uncover regular seasonal movement.
How to calculate the smoothing of a time series?
This leads to: Equation 2 shows that the forecasted value is a weighted average of all past values of the series, with exponentially changing weights as we move back in the series. Basically, we just fit an ARIMA (0,1,1) to the data and determine the α coefficient.
Why is the seasonal effect larger in the second month?
In the case of an increase in the original (not seasonally adjusted) series and a decrease in the seasonally adjusted series, the usual reason is that the seasonal effect for the second month (or quarter) is larger than the seasonal effect for the first month (or quarter).
Seasonal adjustment is the process of estimating and then removing from a time series influences that are systematic and calendar related.
How does Shiskin decomposition of time series work?
The Shiskin decomposition gives graphs of the original series, seasonally adjusted series, trend series, residual (irregular) factors and the between month (seasonal) and within month (trading day) factors that are combined to form the combined adjustment factors.
How are time series models used in production?
From simple spreadsheets to complex financial planning software, modern day companies have many tools to build forecasts using time series data. From traditional time series forecasting to models that use deep learning techniques, there are many solutions. But, the deployment is not straight forward.
How does irregularity affect the direction of a series?
If the magnitude of the irregular component of a series is strong compared with the magnitude of the trend component, the underlying direction of the series can be distorted. However, the major disadvantage of comparing year to year original data, is lack of precision and time delays in the identification of turning points in a series.