Dimensionality of LSTM predictions, inputs and targets
I am writing a thesis on stock price prediction using deep learning, focusing mainly on LSTM variations. I have a time series of the stock returns of 100 stocks over time, and predict a binary classification problem of whether they will go up or down the following day.
My training data is thus of the following shape:
X_train 3D:
(1000 sequences/observations, 200 returns per sequence, 100 stocks) = (1000, 200, 100)
y_train 2D:
(1000 observations, 100 stocks) <- A binary matrix with 1/0 (wether stock went up or down)
Test data:
X_test 3D:
(500 observations, 200 returns per sequence, 100 stocks) = (500,200,100)
LSTM Code:
classifier = Sequential()
classifier.add(LSTM(units, return_sequences = False, input_shape =
(200, 100)))
classifier.add(Dense(units = 100, activation = sigmoid))
classifier.compile()
regressor.fit(X_train, y_train)
probability = classifier.predict(X_test)
classes = classifier.predict_classes(X_test)
When running this through the LSTM the predicted probability has shape (500,100), with each probability in the range [0,1]. This is exactly what i want, one probability per stock for each day in the test period (how likely it is to be of class 1, that is how likely it is to go up the next day).
However, the predicted class has shape (500,), and is in the range [0,100]. I want the predicted class to also be of shape (500,100) and to be binary (0/1).
It seems to me that the model is currently taking each observation, and predicting which stock it is most likely to belong to. This is not what i want, i want it to take each sequence for each stock and predict the binary response for that stock at that time. Therefore, both the probabilities and classes predicted here will be invalid for its intended purpose. Would it be possible to add another dimension to get my desired predictions?
keras deep-learning lstm prediction recurrent-neural-network
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
add a comment |
I am writing a thesis on stock price prediction using deep learning, focusing mainly on LSTM variations. I have a time series of the stock returns of 100 stocks over time, and predict a binary classification problem of whether they will go up or down the following day.
My training data is thus of the following shape:
X_train 3D:
(1000 sequences/observations, 200 returns per sequence, 100 stocks) = (1000, 200, 100)
y_train 2D:
(1000 observations, 100 stocks) <- A binary matrix with 1/0 (wether stock went up or down)
Test data:
X_test 3D:
(500 observations, 200 returns per sequence, 100 stocks) = (500,200,100)
LSTM Code:
classifier = Sequential()
classifier.add(LSTM(units, return_sequences = False, input_shape =
(200, 100)))
classifier.add(Dense(units = 100, activation = sigmoid))
classifier.compile()
regressor.fit(X_train, y_train)
probability = classifier.predict(X_test)
classes = classifier.predict_classes(X_test)
When running this through the LSTM the predicted probability has shape (500,100), with each probability in the range [0,1]. This is exactly what i want, one probability per stock for each day in the test period (how likely it is to be of class 1, that is how likely it is to go up the next day).
However, the predicted class has shape (500,), and is in the range [0,100]. I want the predicted class to also be of shape (500,100) and to be binary (0/1).
It seems to me that the model is currently taking each observation, and predicting which stock it is most likely to belong to. This is not what i want, i want it to take each sequence for each stock and predict the binary response for that stock at that time. Therefore, both the probabilities and classes predicted here will be invalid for its intended purpose. Would it be possible to add another dimension to get my desired predictions?
keras deep-learning lstm prediction recurrent-neural-network
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
Did you check this and this? You can adjust your problem accordingly.
– ARAT
Nov 18 '18 at 16:31
add a comment |
I am writing a thesis on stock price prediction using deep learning, focusing mainly on LSTM variations. I have a time series of the stock returns of 100 stocks over time, and predict a binary classification problem of whether they will go up or down the following day.
My training data is thus of the following shape:
X_train 3D:
(1000 sequences/observations, 200 returns per sequence, 100 stocks) = (1000, 200, 100)
y_train 2D:
(1000 observations, 100 stocks) <- A binary matrix with 1/0 (wether stock went up or down)
Test data:
X_test 3D:
(500 observations, 200 returns per sequence, 100 stocks) = (500,200,100)
LSTM Code:
classifier = Sequential()
classifier.add(LSTM(units, return_sequences = False, input_shape =
(200, 100)))
classifier.add(Dense(units = 100, activation = sigmoid))
classifier.compile()
regressor.fit(X_train, y_train)
probability = classifier.predict(X_test)
classes = classifier.predict_classes(X_test)
When running this through the LSTM the predicted probability has shape (500,100), with each probability in the range [0,1]. This is exactly what i want, one probability per stock for each day in the test period (how likely it is to be of class 1, that is how likely it is to go up the next day).
However, the predicted class has shape (500,), and is in the range [0,100]. I want the predicted class to also be of shape (500,100) and to be binary (0/1).
It seems to me that the model is currently taking each observation, and predicting which stock it is most likely to belong to. This is not what i want, i want it to take each sequence for each stock and predict the binary response for that stock at that time. Therefore, both the probabilities and classes predicted here will be invalid for its intended purpose. Would it be possible to add another dimension to get my desired predictions?
keras deep-learning lstm prediction recurrent-neural-network
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
I am writing a thesis on stock price prediction using deep learning, focusing mainly on LSTM variations. I have a time series of the stock returns of 100 stocks over time, and predict a binary classification problem of whether they will go up or down the following day.
My training data is thus of the following shape:
X_train 3D:
(1000 sequences/observations, 200 returns per sequence, 100 stocks) = (1000, 200, 100)
y_train 2D:
(1000 observations, 100 stocks) <- A binary matrix with 1/0 (wether stock went up or down)
Test data:
X_test 3D:
(500 observations, 200 returns per sequence, 100 stocks) = (500,200,100)
LSTM Code:
classifier = Sequential()
classifier.add(LSTM(units, return_sequences = False, input_shape =
(200, 100)))
classifier.add(Dense(units = 100, activation = sigmoid))
classifier.compile()
regressor.fit(X_train, y_train)
probability = classifier.predict(X_test)
classes = classifier.predict_classes(X_test)
When running this through the LSTM the predicted probability has shape (500,100), with each probability in the range [0,1]. This is exactly what i want, one probability per stock for each day in the test period (how likely it is to be of class 1, that is how likely it is to go up the next day).
However, the predicted class has shape (500,), and is in the range [0,100]. I want the predicted class to also be of shape (500,100) and to be binary (0/1).
It seems to me that the model is currently taking each observation, and predicting which stock it is most likely to belong to. This is not what i want, i want it to take each sequence for each stock and predict the binary response for that stock at that time. Therefore, both the probabilities and classes predicted here will be invalid for its intended purpose. Would it be possible to add another dimension to get my desired predictions?
keras deep-learning lstm prediction recurrent-neural-network
keras deep-learning lstm prediction recurrent-neural-network
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
asked Nov 17 '18 at 13:34
Jonas Snefjellå LøvåsJonas Snefjellå Løvås
11
11
Did you check this and this? You can adjust your problem accordingly.
– ARAT
Nov 18 '18 at 16:31
add a comment |
Did you check this and this? You can adjust your problem accordingly.
– ARAT
Nov 18 '18 at 16:31
Did you check this and this? You can adjust your problem accordingly.
– ARAT
Nov 18 '18 at 16:31
Did you check this and this? You can adjust your problem accordingly.
– ARAT
Nov 18 '18 at 16:31
add a comment |
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Did you check this and this? You can adjust your problem accordingly.
– ARAT
Nov 18 '18 at 16:31