90% complete lstm

lstm.py

+from keras.preprocessing.sequence import TimeseriesGenerator
+from sklearn.preprocessing import MinMaxScaler
+
+import numpy as np
+import pandas as pd
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
+from statsmodels.tsa.seasonal import seasonal_decompose
+from pmdarima import auto_arima
+from sklearn.metrics import mean_squared_error
+from statsmodels.tools.eval_measures import rmse
+import warnings
+warnings.filterwarnings("ignore")
+df = pd.read_csv('monthly-beer-production-in-austr.csv')
+
+train_data = df[:len(df)-12]
+test_data = df[len(df)-12:]
+
+scaler = MinMaxScaler()
+scaler.fit(train_data)
+scaled_train_data = scaler.transform(train_data)
+scaled_test_data = scaler.transform(test_data)
+
+n_input = 12
+n_features= 1
+generator = TimeseriesGenerator(scaled_train_data, scaled_train_data, length=n_input, batch_size=1)
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+
+lstm_model = Sequential()
+lstm_model.add(LSTM(200, activation='relu', input_shape=(n_input, n_features)))
+lstm_model.add(Dense(1))
+lstm_model.compile(optimizer='adam', loss='mse')
+
+lstm_model.summary()
+
+lstm_model.fit_generator(generator,epochs=20)
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