ARIMA模型，参数含义参考：https://www.cnblogs.com/bradleon/p/6827109.html
from statsmodels.tsa.arima_model import ARIMA plt.figure(figsize = (15,8)) model = ARIMA(Train_log, order = (2,1,0)) #here q value is zero since it is just AR Model
SARIMAX Model，多元季节性时间序列模型，用于预测与异常诊断，参考博客：https://blog.csdn.net/weixin_41512727/article/details/82999831
import statsmodels.api as sm y_hat_avg = valid.copy() fit1 = sm.tsa.statespace.SARIMAX(Train.Count, order = (2,1,4), seasonal_order =(0,1,1,7)).fit() y_hat_avg[‘SARIMA‘] = fit1.predict(start="2014-6-25", end="2014-9-25", dynamic=True)
LSTM Model
import numpy as np from numpy import newaxis from keras.layers.core import Dense, Activation, Dropout from keras.layers.recurrent import LSTM from keras.models import Sequential my_model = Sequential() my_model.add(LSTM( input_shape=(None, 1), units=50, return_sequences=True)) my_model.add(LSTM(100, return_sequences=False)) my_model.add(Dropout(0.5)) my_model.add(Dense(1)) my_model.add(Activation(‘linear‘)) my_model.compile(loss=‘mse‘, optimizer=‘rmsprop‘) # Fill in the parameters to fit your model my_model.fit( X_train, y_train, batch_size=1024, # Fill this in epochs=1, # Fill this in validation_split=0.05)
Kaggle Learn Time Series Modeling 学习小计
原文：https://www.cnblogs.com/xbit/p/10184176.html