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Among the time series models, which model can better fit the analysis and prediction of volatility?
Principal component analysis (PCA) is to promote several typical principal components among multiple indicators, and one method to calculate the score of principal components is regression method.

The basic idea of ARIMA model is to regard the data sequence formed by the predicted object over time as a random sequence, which is approximately described by a certain mathematical model. Once this model is identified, the future value can be predicted from the past and present values of the time series. Modern statistical methods and econometric models have been able to help enterprises predict the future to some extent.

ARIMA model is based on historical data, so the more historical data collected, the more accurate the model will be.

Monthly savings data can be regarded as a random time series formed over time. By analyzing the randomness, stationarity and seasonality of the savings values in this time series, a mathematical model is used to describe the correlation or dependence between these monthly savings values, so that the information of past and present savings values can be used to predict the future savings situation.