Applying Time Series Analysis Builds Stock Price Forecast Model

Abstract

⎪ ⎩ The abbreviated formula is () () d t t B x B θ ε ϕ ∇ = , { } t ε zero average value white noise series. () 2 1 2 1 p p B B B B ϕ ϕ ϕ ϕ = − − − − L is the autoregressive coefficient polynomial. () 2 1 2 1 q q B B B B θ θ θ θ = − − − − L is the moving average coefficient polynomial. () 1 d d B ∇ = − , ∇ is the difference operator, d is the difference order, B is backward shift operator 1 t t Bx x − =. 2. ARIMA model modeling step 2.1 Data processing First judge the series whether to be steady or not by observing the autocorrelation coefficient figure and the partial autocorrelation coefficient figure. Second if the series is not steady, carries on the series difference or the season