Models in Time Series Analysis enable the user to generate: forecasts of a (dependent) time series that is based upon the information of its own past, explain events that occurred in the past, and provide insight into the dynamical interrelationships between variables.
The analysis model of the time series can be used to explain the dynamic relationship between past events and in-depth analysis variables based on the previous data prediction time sequence. In the following sections we describe the development of Autoregressive Integrated Moving Average models (short: ARIMA), Transfer Function-Noise models, and Multivariate Time Series Models according to the methodologies proposed by Box and Jenkins and many other scientists.
Here we According to Box and Jenkins and a few other scientists about: ARIMA model; Transfer Function-Noise model; Multivariate Time Series model; For obvious reasons these methodologies can only apply to time series Above that, the steps or intervals of the time. Series Under Investigation Are ARES Supposed To Be Equally Space (Which is an important rest).
These models can only be used for time series because of the obvious reasons. More importantly, for the study of time series, we are based on such a premise: the interval of the time series is equal. Furthermore We Assume That Each Observation of The Time Series Has The Same Expection Function, Standard Deviation, and Probability Distribution function.
In addition, we have also assumed that these time sequences have this same expectation function, standard deviation and the same probability separate function; Since The Box-Jenkins Methodology Uses Maximum Likelihood Estimation (mle), IT Is Oous That A Distribution Has To Be Assumed About the error term. In practice we will assume a white noise error component, which is a sequence of uncorrelated stochastic variables with a fixed (normal) distribution, a mathematical expectation of zero, and constant variance.
