Adjusting a time series to measure and remove the regular seasonal components (e.g. the effect of Christmas on food retail sales).
Adjustment of time-series data to eliminate the effect of intra-year variations which tend to occur each year in approximately the same manner. Examples of such variations include school terms, holidays, weather patterns, etc.
A statistical method often used to adjust house price data to reflect the seasonal nature of house selling/buying. It is not unusual for seasonal adjustment to bias figures in unusual ways.
Seasonal adjustment is a statistical technique which eliminates the influences of weather, holidays, the opening and closing of schools, and other recurring seasonal events from economic time series. This permits easier analysis of cyclical, trend, and other non-seasonal movements in the data. By eliminating seasonal fluctuations, the series becomes smoother and it is easier to compare data from month to month.
a statistical adjustment made to accommodate predictable fluctuations as a function of the season of the year; "seasonal adjustments for housing starts must be made in mid-winter"
the procedure of applying smoothing factors to an economic data series to remove the effects of routine fluctuations associated with weather and institutional schedules such as the school year.
A statistical technique used to remove the effect of normal seasonal fluctuations in data so underlying trends become more evident. For example, the seasonally-adjusted unemployment rate smoothes out the changes in unemployment due to the typical seasonal hiring in the summer and layoffs in the winter for workers in industries such as agriculture and construction.
Adjustment of time series data to remove cyclical effects caused by periodic seasonal factors. Data such as unemployment rates, which tend to be higher at certain times of the year, are often adjusted to allow better comparison across months. In a multiplicative adjustment, each data value is divided by the estimated seasonal index for the corresponding month. Seasonal indices are scaled so that an average month has an index of 100. An index of 110 would indicate a season 10% above average, while an index of 90 would indicate a season 10% below average. In an additive adjustment, an average month has an index of 0. In the latter method, the index is subtracted from the data value to form the adjusted data.
A statistical technique for removing seasonal fluctuations from a data series. For example, in the case of market pulp, monthly shipments are usually at their lowest during the third quarter of the year and at their highest during the fourth quarter. By adjusting the data for this predictable seasonal fluctuation, the underlying trend can be more easily seen.
Seasonal adjustment removes the effects of events that follow a more or less regular pattern each year. These adjustments make it easier to observe the cyclical and other non-seasonal movements in a data series.
Seasonal adjustment is a statistical method for time series analysis.
