Python ARIMA
时间: 2023-11-05 19:19:37 浏览: 48
ARIMA (AutoRegressive Integrated Moving Average) is a popular time series forecasting method in statistics and econometrics. It is used to model and predict future values based on the historical data patterns. In Python, you can use the 'statsmodels' library to implement ARIMA models.
Here's a simple example of how to use ARIMA in Python:
```python
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
# Load the time series data
data = pd.read_csv('data.csv')
# Fit the ARIMA model
model = ARIMA(data, order=(p, d, q))
model_fit = model.fit()
# Forecast future values
forecast = model_fit.forecast(steps=n)
# Print the forecasted values
print(forecast)
```
In the above code, you need to replace 'data.csv' with your actual time series data file. Also, you need to specify the values for the order parameter, denoting the number of autoregressive (p), integrated (d), and moving average (q) terms.
I hope this helps you understand how to use Python for ARIMA modeling. Let me know if you have any further questions!