ARIMA 模型使用-for time series model时间序列模型

ARIMA 模型使用-for time series model

Step1. Stationary check

平稳性检验有非常严格的标准。

1.恒定的均值

2.恒定的方差

3.自协方差与时间无关

the autocovariance is a function that gives the covariance of the process with itself at pairs of time points

平稳定的检验方法：

1.画滑动平均的统计rolling statistics,We can plot the moving average or moving variance and see if it varies with time

2.Dickey-Fuller Test: This is one of the statistical tests for checking stationarity. Here the null hypothesis is that the TS is non-stationary. The test results comprise of a Test Statistic and some Critical Values for difference confidence levels. If the ‘Test Statistic’ is less than the ‘Critical Value’, we can reject the null hypothesis and say that the series is stationary. Refer this article for details.https://machinelearningmastery.com/time-series-data-stationary-python/

Setp2: 让序列变得平稳

1.趋势

2.季节性因素。

消除趋势性

1.log

2.moving average

Value - moving average

3.diff

4.decomposing 分解

Reference:

[1] https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/