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Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
SPSS: IBM: 28.0 (24 May 2021 ()) No Proprietary: CLI, GUI: Java, C, C++, Fortran ... Time series analysis. Support for various time series analysis methods. Product ARIMA
jamovi – A free software alternative to IBM SPSS Statistics; JASP – A free software alternative to IBM SPSS Statistics with additional option for Bayesian methods; JMulTi – For econometric analysis, specialised in univariate and multivariate time series analysis
In time series analysis, the Box–Jenkins method, [1] named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series.
SPSS 30: JASP 0.19.3: SPSS 30: Analysis: Classic: Classic: Bayesian: ... BSTS: Bayesian take on linear Gaussian state space models suitable for time series analysis.
Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function.Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.
In time series analysis used in statistics and econometrics, autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models are generalizations of the autoregressive moving average (ARMA) model to non-stationary series and periodic variation, respectively.
Time series [ edit ] The initial stages in the analysis of a time series may involve plotting values against time to examine homogeneity of the series in various ways: stability across time as opposed to a trend; stability of local fluctuations over time.