TFLearn時間序列預測預測

定義我的神經網絡和訓練我的模型後：TFLearn時間序列預測預測

net = tflearn.input_data(shape=[None, 1, 1]) 
tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0) 
net = tflearn.lstm(net, timesteps, dropout=0.8) 

net = tflearn.fully_connected(net, 1, activation='linear', weights_init=tnorm) 
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001, 
          loss='mean_square', metric='R2') 
# Define model 
model = tflearn.DNN(net, clip_gradients=0.) 
model.fit(X, y, n_epoch=nb_epoch, batch_size=batch_size, shuffle=False, show_metric=True) 
score = model.evaluate(X, y, batch_size=128) 
model.save('ModeSpot.tflearn')

現在我有一個問題，大部分的教程，我發現這樣做的時間序列預測使用的測試設置預測（他們將測試集設置爲.predict（））。問題是，實際上我們不知道這一點，因爲這是我們想要預測的。

現在我使用：

def forecast_lstm(model, X): 
    X = X.reshape(len(X), 1, 1) 
    yhat = model.predict(X) 
    return yhat[0, 0] 

# split data into train and test-sets 
    train, test = supervised_values[0:-10000], supervised_values[-10000:] 

    # transform the scale of the data 
    scaler, train_scaled, test_scaled = scale(train, test) 

    # Build neural network 
    net = tflearn.input_data(shape=[None, 1, 1]) 
    tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0) 
    net = tflearn.lstm(net, 1, dropout=0.3) 
    net = tflearn.fully_connected(net, 1, activation='linear', weights_init=tnorm) 
    net = tflearn.regression(net, optimizer='adam', learning_rate=0.001, 
          loss='mean_square', metric='R2') 
    lstm_model = tflearn.DNN(net, clip_gradients=0.) 
    lstm_model.load('ModeSpot.tflearn') 

    # forecast the entire training dataset to build up state for forecasting 
    train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1) 
    lstm_model.predict(train_reshaped) 
    # walk-forward validation on the test data 
    predictions = list() 
    error_scores = list() 
    for i in range(len(test_scaled)): 
     # make one-step forecast 
     X, y = test_scaled[i, 0:-1], test_scaled[i, -1] 
     yhat = forecast_lstm(lstm_model, X) 
     # invert scaling 
     yhat2 = invert_scale(scaler, X, yhat) 
     # # invert differencing 
     yhat3 = inverse_difference(raw_values, yhat2, len(test_scaled) + 1 - i) 
     # store forecast 
     predictions.append(yhat3)

但只爲我的測試集工作。我該如何預測下一個x值？我想我已經看到某處預測T值的地方，我將不得不使用T-1的值作爲預測值（然後T對於T + 1，並且像這樣，直到達到我想要的預測數）。這是一個好方法嗎？

我試着這樣做：

def forecast_lstm2(model, X): 
    X = X.reshape(-1, 1, 1) 
    yhat = model.predict(X) 
    return yhat[0, 0] 

test = list() 
X, y = train_scaled[0, 0:-1], train_scaled[0, -1] 
test.append(X) 
for i in range(len(test_scaled)): 
    # make one-step forecast 
    yhat = forecast_lstm2(lstm_model, test[i]) 
    test.append(yhat) 

    # invert scaling 
    yhat2 = invert_scale(scaler, test[i+1], yhat) 
    # # invert differencing 
    yhat3 = inverse_difference(raw_values, yhat2, len(test) + 1 - i) 
    # store forecast 
    predictions.append(yhat3)

但它並沒有收到預期的效果（一些預測後，總是給出相同的結果）。

感謝您的關注和時間。

來源

2017-07-27 Eric Godard

最後，這似乎工作：＃使單步預測 DEF forecast_lstm2（型號，X）： X = X.reshape（-1，1，1） yhat = model.predict（ X） return yhat [0,0]

def prediction(spotId): 
    epoch = [5, 15, 25, 35, 45, 50, 100] 
    for e in epoch: 
     tf.reset_default_graph() 

     # Load CSV file, indicate that the first column represents labels 
     data = read_csv('nowcastScaled'+str(spotId)+'.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser) 

     # transform data to be stationary 
     raw_values = data.values 
     diff_values = difference(raw_values, 1) 

     # transform data to be supervised learning 
     supervised = timeseries_to_supervised(diff_values, 1) 
     supervised_values = supervised.values 

     # split data into train and test-sets (I removed the testing data from the excel file) 
     train = supervised_values[x:] 

     # transform the scale of the data (and removed anything related to testing set) 
     scaler, train_scaled = scale(train) 
     # Build neural network 
     net = tflearn.input_data(shape=[None, 1, 1]) 
     tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0) 
     net = tflearn.lstm(net, 1, dropout=0.8) 
     net = tflearn.fully_connected(net, 1, activation='linear', weights_init=tnorm) 
     net = tflearn.regression(net, optimizer='adam', learning_rate=0.0001, 
            loss='mean_square', metric='R2') 
     lstm_model = tflearn.DNN(net, clip_gradients=0.) 
     lstm_model.load('ModeSpot'+str(spotId)+'Epoch'+str(e)+'.tflearn') 

     # forecast the entire training dataset to build up state for forecasting 
     train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1) 
     lstm_model.predict(train_reshaped) 
     # walk-forward validation on the test data 
     predictions = list() 
     predictionFeeder = list() 
     X, y = train_scaled[0, 0:-1], train_scaled[0, -1] 
     predictionFeeder.append(X) 
     for i in range(0, 10000): 
      # make one-step forecast 
      yhat = forecast_lstm2(lstm_model, predictionFeeder[i]) 
      predictionFeeder.append(yhat) 
      # invert scaling 
      yhat2 = invert_scale(scaler, predictionFeeder[i + 1], yhat) 
      yhat3 = inverse_difference(raw_values, yhat2, 10000 + 1 - i) 
      predictions.append(yhat3)

來源

2017-08-04 02:34:20

TFLearn時間序列預測預測

回答

相關問題