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LSTM
Architecture => val_RMSE, RMSE on 25nov load
LSTM(1) + LSTM(1) + Dense() => 130
model.add(GRU(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True))
172.30510765803461 70.23247637069744
model.add(GRU(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True))
169.01878138738027 98.78463527925186
model = Sequential()
model.add(GRU(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True))
model.add(Dense(train_y.shape[1]))
179.42414235781897 137.3227309920991
model = Sequential()
model.add(GRU(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True, return_sequences=True))
model.add(GRU(1, stateful=True))
model.add(Dense(train_y.shape[1]))
model.add(Dropout(0.2))
model.add(Dense(train_y.shape[1]))
179.68186896684816 115.50372901507328
batch_size = 1
model = Sequential()
model.add(GRU(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2])))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=3,
verbose=0, mode='auto')
161.98254979093554 111.68976793136368
del model
batch_size = 1
model = Sequential()
model.add(GRU(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=3,
verbose=0, mode='auto')
170.49926684704036 77.18821593330154
model.add(LSTM(1, batch_input_shape=(1, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(LSTM(1, stateful=True, return_sequences=True))
model.add(LSTM(1, stateful=True))
model.add(Dense(train_y.shape[1]))
179.72401621324363 115.36772988379201
model = Sequential()
model.add(LSTM(1, batch_input_shape=(1, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(LSTM(1, stateful=True, return_sequences=True))
model.add(LSTM(1, stateful=True))
model.add(Dense(train_y.shape[1]))
model.add(Dense(train_y.shape[1]))
179.6798363877226 115.55969177210693
del model
model = Sequential()
model.add(LSTM(1, batch_input_shape=(1, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(LSTM(1, stateful=True, return_sequences=True))
model.add(LSTM(1, stateful=True))
model.add(Dense(train_y.shape[1]))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
179.64784244480092 115.40448187064597
model = Sequential()
model.add(LSTM(1, batch_input_shape=(1, train_x.shape[1], train_x.shape[2])))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
179.64784244480092 115.40448187064597
model = Sequential()
model.add(LSTM(1, batch_input_shape=(1, train_x.shape[1], train_x.shape[2]), stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
170.15684534737213 73.97455785821403
del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
179.69537398840617 119.75008940730767
model = Sequential()
model.add(SimpleRNN(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True))
179.4834537383419 116.1263437001296
model = Sequential()
model.add(SimpleRNN(2, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(SimpleRNN(2, stateful=True, return_sequences=True))
model.add(SimpleRNN(2, stateful=True))
model.add(Dense(train_y.shape[1]))
179.5914806001997 131.20615554368203
# del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True))
model.add(Dense(train_y.shape[1]))
model.add(Dropout(0.2))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
356.5411939524292 348.51289967287505
# del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True, return_sequences=True))
model.add(SimpleRNN(1, stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
179.9243505278806 127.67319742877328
# del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
176.81005209413934 127.72322737276058
del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(2, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
169.50042814212946 75.2211069885581
del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(3, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
175.55278890307227 110.81766216199584
del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(2, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2]), stateful=True, return_sequences=True))
model.add(SimpleRNN(2, stateful=True))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='sgd')
176.05695242370095 112.45128976645987
nlags=7
SimpleRNN(1)
167.12253483672743 164.3591296684758
SimpleRNN(2)
168.12847130886865 187.35787301304964
nlags=15
137.52007268769108 151.11466939045795
nlags=20
160.61520803024845 227.80558679708057
del model
batch_size = 1
model = Sequential()
model.add(SimpleRNN(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2])))
model.add(Dense(train_y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='adam')
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=3,
verbose=0, mode='auto')
model fit for 20 epochs, shuffle =True, not stateful