قواعد ضروية لاختيار نموذج السلسلة الزمنية Rules for Specifying the TS Model

يشرح هذا المقطع القواعد الأساسية التي ينبغي أن يسترشد بها الباحث في السلاسل الزمنية كي يوصف نموذجه بطريقة سلسمة How to Specify a Time Series AR, MA or ARMA Time series models are used to analyze and forecast data that changes over time. The process of specifying a time series model involves selecting an appropriate model that can effectively capture the underlying patterns in the data. The following are some general rules for specifying a time series model: Stationarity: The data series should be stationary, which means that its mean and variance should be constant over time. Stationarity can be checked by plotting the data over time and looking for any trends, seasonality, or other patterns that might indicate non-stationarity. Autocorrelation: Autocorrelation measures the degree to which a time series is correlated with itself over time. A model with high autocorrelation indicates that the values at one time point are highly correlated with the values at other time points. Autocorrelation can be checked by plotting the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Lags: Lags refer to the number of time periods that should be included in the model. The choice of the appropriate number of lags depends on the specific time series and can be determined by examining the ACF and PACF plots. Residuals: Residuals are the difference between the actual values and the predicted values from the model. A good time series model should have residuals that are normally distributed with a mean of zero and constant variance over time. Residuals can be checked by plotting the residuals over time and examining the pattern. Information Criteria: There are several information criteria that can be used to compare different time series models and select the best one. The most commonly used information criteria are the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). By following these rules, a suitable time series model can be specified that can provide accurate forecasts and insights into the underlying patterns and trends in the data.