<jupyter_start><jupyter_text>AR + MA models1. 「AR」*Autoregression*，自回归模型；2. 「MA」*Moving Average*，滑动平均模型；3. 「ARMA」*Autoregressive Moving Average*，自回归滑动平均模型；4. 「ARIMA」*Autoregressive Integrated Moving Average*，差分自回归移动平均模型；5. 「SARIMA」*Seasonal Autoregressive Integrated Moving-Average*，季节性差分自回归移动平均模型；6. 「SARIMAX」*Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors*，外生变量的季节性差分自回归移动平均模型；7. 「VAR」*Vector Autoregression*，向量自回归模型；8. 「VARMA」*Vector Autoregression Moving-Average*，向量自回归滑动平均模型；9. 「VARMAX」*Vector Autoregression Moving-Average with Exogenous Regressors*，外生变量的向量自回归滑动平均模型；  AR + MA<jupyter_code># AR example
from statsmodels.tsa.ar_model import AutoReg
from random import random
# contrived dataset
data = [x + random() for x in range(1, 100)]
# fit model
model = AutoReg(data, lags=1)
model_fit = model.fit()
# make prediction
yhat = model_fit.predict(len(data), len(data))
print(yhat)<jupyter_output>[100.69828944]
{'fraction_non_alphanumeric': 0.12156448202959831, 'fraction_numerical': 0.026427061310782242, 'mean_word_length': 8.565656565656566, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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