学习小计: Kaggle Learn Time Series Modeling

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)

巴特西

学习小计: Kaggle Learn Time Series Modeling

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