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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.
Removing the seasonal component directs focus on other components and will allow better analysis. [5] Different statistical research groups have developed different methods of seasonal adjustment, for example X-13-ARIMA and X-12-ARIMA developed by the United States Census Bureau; TRAMO/SEATS developed by the Bank of Spain; [6] MoveReg (for ...
Seasonal subseries plots enables the underlying seasonal pattern to be seen clearly, and also shows the changes in seasonality over time. [2] Especially, it allows to detect changes between different seasons, changes within a particular season over time. However, this plot is only useful if the period of the seasonality is already known. In ...
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]
Likewise, seasonal differencing is applied to a seasonal time-series to remove the seasonal component. From the perspective of signal processing, especially the Fourier spectral analysis theory, the trend is a low-frequency part in the spectrum of a series, while the season is a periodic-frequency part.
The technique for time series decomposition. In this step, a researcher can add different state variables: trend, seasonality, regression, and others. Spike-and-slab method. In this step, the most important regression predictors are selected. Bayesian model averaging. Combining the results and prediction calculation.
The start and end of astronomical and solar seasons depend on variables that can differ slightly every year, so weather forecasters also use a system that is more consistent on a year-to-year basis.
In policy analysis, forecasting future production of biofuels is key data for making better decisions, and statistical time series models have recently been developed to forecast renewable energy sources, and a multiplicative decomposition method was designed to forecast future production of biohydrogen. The optimum length of the moving average ...