3.1 Preliminary analysis of data and modeling identification

Time series prediction is a statistical method processing dynamic data, which is a random sequence arranged in chronological order or a set of ordered random variables defined in probabilistic space {Xt, t = 1, 2, …, n}, in which the parameter t represents time. In the TSM, if the samples' autocorrelation function f g ρ^<sup>k</sup> decreases to zero based on the negative exponential function, then it can be preliminarily judged that this sequence is a stationary autoregressive moving average model (ARMA). If the absolute value of the sample autocorrelation function in the q-step delay ρ^kð Þ k≤ q is greater than twice of the standard deviation and the value of ρ^kð Þ k . q is less than twice of the standard deviation, then the sequence is q-step moving average model (MA(q)). In a similar vein, we can judge p-step autoregressive moving average model (AR(p)) according to the truncation situation of partial autocorrelation function f g φ^kk .
