When using Python to implement the ARIMA model, it is critical to choose the appropriate p, d, and q parameters. This article will introduce an ARIMA parameter selection method based on time series analysis and statistical methods, and provide the corresponding Python code.
ARIMA models are a powerful tool for forecasting time series data. The core of the ARIMA model is its three parameters: p, d, q. Among them, p represents the order of the time series autoregressive part; q represents the order of the time series moving average part; d represents the number of differences. It is very important to choose these parameters correctly because these parameters can affect the predictive performance of the model.
So how do you choose the correct ARIMA parameters? The most common approach is to use ACF plots and PACF plots. The ACF plot represents the autocorrelation function, and the PACF plot represents the partial autocorrelation function. They can help us choose appropriate p, q values, while the d value can be determined by comparing the stability of the original time series and the difference time series.
Next, let's look at a simple example, assuming we want to predict the temperature of a certain city for the next week.
First, we need to import the relevant library and read the data:
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import acf