ARIMA
Key word: Model, Time Series Usage: A statistical analysis model that uses time series data to either better understand the data set or to better predict future trends.
Background
Stationary and differencing
Stationary time series does not depend on time at which the series is observed.
Stationary time series will not have any predictable patterns until a cycle is observed.
The procedure of fitting a time series to a proper model is termed as Time Series Analysis.
Algorithm
Arima itself is to acquire a sequence of data based on previous observations on time series.
生成 ARIMA 模型的基本步骤：
- 对序列绘图，进行 ADF 检验，观察序列是否平稳；对于非平稳时间序列要先进行 d 阶差分，转化为平稳时间序列；
- 经过第一步处理，已经得到平稳时间序列。要对平稳时间序列分别求得其自相关系数（ACF）和偏自相关系数（PACF），通过对自相关图和偏自相关图的分析，得到最佳的阶数p、q；
- 由以上得到的d、q、p ，得到 ARIMA 模型。然后开始对得到的模型进行模型检验。
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