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Seasonal adjustment or deseasonalization is a statistical method for removing the seasonal component of a time series. It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components.
This is an important technique for all types of time series analysis, especially for seasonal adjustment. [2] It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behavior.
The seasonally adjusted annual rate (SAAR) is a rate that is adjusted to take into account typical seasonal fluctuations in data and is expressed as an annual total. SAARs are used for data affected by seasonality, when it could be misleading to directly compare different times of the year. SAARs are often used for car sales.
However, the BLS noted that before a seasonal adjustment, gasoline prices actually fell 5.8% last month. Read more: Our guide to the best cash-back credit cards for gas stations, groceries, and more
After seasonal adjustment, the national index increased 0.3 percent month-over-month, while the 10-city index rose 0.1 percent and the 20-city group was up 0.2 percent from the previous month.
X-13ARIMA-SEATS, successor to X-12-ARIMA and X-11, is a set of statistical methods for seasonal adjustment and other descriptive analysis of time series data that are implemented in the U.S. Census Bureau's software package. [3]
In time series data, seasonality refers to the trends that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays [1] and consists of periodic, repetitive, and generally regular and predictable patterns in the levels [2] of a time series.
The Berlin procedure (BV) is a mathematical procedure for time series decomposition and seasonal adjustment of monthly and quarterly economic time series. The mathematical foundations of the procedure were developed in 1960's at Technische Universität Berlin and the German Institute for Economic Research (DIW).