python - TFLearn 时间序列预测 预测

Question

定义我的神经网络并训练我的模型后:

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')

我现在遇到了一个问题，我发现进行时间序列预测的大部分教程都使用测试集来预测(他们将测试集提供给 .predict())。问题是，实际上我们不知道这一点，因为这是我们想要预测的。

现在我正在使用它:

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)

但它只适用于我的测试集。我该如何预测下一个 x 值？我想我在某个地方看到过，要预测 T 处的值，我必须使用 T-1 处的值进行预测(然后使用 T 处的值进行 T+1 等，直到达到我想要的预测数量)。这是一个好方法吗？

我尝试过这样做:

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)

但它没有达到预期的效果(经过一些预测，它总是给出相同的结果)。

感谢您的关注和时间。

Answer 1

最后这似乎有效: # 进行一步预测 def Forecast_lstm2(模型, X): X = X.reshape(-1, 1, 1) yhat = model.predict(X) 返回 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)