LSTM to multivariate sequence classification

Question

LSTM to multivariate sequence classification

okuoub

2022年5月17日 11:35

How can I train multivariate to multiclass sequence using LSTM in keras?

I have 50000 sequences, each in the length of 100 timepoints. At every time point, I have 3 features (So the width is 3).

I have 4 classes and I want to bulid a classifier to determine class for sequence. What is the best way to do so?

I saw many guides for univariate sequence classification but none for multivariate, and I don't know how to apply this on the multivariate case

Topic lstm keras sequence time-series python

Category Data Science

Christine Chetty · Accepted Answer · 2022年5月17日 11:35

1

Christine Chetty answered at 2022年5月17日 11:35

In addition, this article explains some generalized methods for clustering using DTW on multivariate data. Especially useful for IOT data

https://link.springer.com/article/10.1007/s10618-020-00727-3

vignesh_md · Accepted Answer · 2020年10月31日 16:24

Since you are using LSTMs for classification using the multivariate time series data, you need to model your time-series data into a supervised learning problem and specify the previous time steps you need to look before by specifying the time-lag count
You need to look into the to_supervised function and specify the number of outputs your model has. In your case, it is 4.
Split your train and test set from the whole set of data. A 70:30 ratio for the train, test would be a good start.
Also, note that you need to scale your values using the sklearn.preprocessing.MinMaxScaler() function.
You need to reshape your train and test values into (batch_size/sample_size, time_steps, feature_size) as an LSTM Layer in Keras expects your data to be fed in a 3D array format.
For eg: Your training shape would be train.shape = (batch_size, 100, 3)

And for the model building in Keras

model = Sequential()
model.add(LSTM(number_of_hidden_units, activation='relu', input_shape=(n_timesteps = 100, n_features = 3))) 
model.add(Dense(4, activation='softmax')) #since number of output classes is 4
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, validation_data=(X_test, y_test), no_of_epochs, batch_size)

Note that I have just given a rough outline of the model building and left out the hyperparameters at your convenience. You can either stack more LSTM layers onto the model or tune the number of hidden_layers in the model Refer Multi-Class Classification for more details on it.

LSTM to multivariate sequence classification

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