exchange_rate.txt

[JonathanHuangC]

What do the 8 columns in exchange_rate.txt represent?
Where did this data come from?
Thank you for your response.

[philipperemy]

@JonathanHuangC Good question. It comes from this repo. I've transferred your question.

laiguokun/multivariate-time-series-data#7

[JonathanHuangC]

@philipperemy
Is there any example of using TCN to extract features?
For example, compressing the exchange rate of [1000 * 8] into [1 * 8], where 1000 represents the day and 8 is the feature of the data.
Thank you very much for your reply.

[philipperemy]

You mean some form of auto encoding? You can just search for LSTM features extraction and you swap the LSTM class with the TCN class and it should work.

[JonathanHuangC]

You mean some form of auto encoding? -> Yes, it seems to be using TCN to implement the auto encoder.

Do you mean that it is enough to change LSTM to TCN? Sorry for there are a lot of questions.

Thank you again for your reply.

LSTM autoencoder

define model

model = Sequential()

Encoder step

model.add(LSTM(15, input_shape=(X_train.shape[1], X_train.shape[2]), activation='relu'))
model.add(RepeatVector(X_train.shape[1]))

Decoder step

model.add(LSTM(15, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(X_train.shape[2])))

model.compile(optimizer='adam', loss='mse')

history = model.fit(X_train, X_train, epochs=_epochs, batch_size = _batch_size,
validation_split=_validation_split, callbacks=callback)

TCN autoencoder

define model

model = Sequential()

Encoder step

model.add(TCN(15, input_shape=(X_train.shape[1], X_train.shape[2]), activation='relu'))
model.add(RepeatVector(X_train.shape[1]))

Decoder step

model.add(TCN(15, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(X_train.shape[2])))

model.compile(optimizer='adam', loss='mse')

history = model.fit(X_train, X_train, epochs=_epochs, batch_size = _batch_size,
validation_split=_validation_split, callbacks=callback)

[philipperemy]

yeah it's as easy as swapping the class.