I have several different time-series of different shapes(same number of samples), how would I train a model using several of these?
For example, say
>>>Series_1.shape (29276, 32, 4) >>>Series_2.shape (29276, 5, 14)
The first layer in the model would look like:
self.CNN = tf.keras.Sequential([ layers.Conv1D(filters=32, kernel_size=3, activation="relu", input_shape=(features, steps), padding='same'), layers.MaxPool1D(pool_size=3, padding='same'), # ... etc
input_shape would only work on one input. Am I supposed to use a 2D Convolution for this task?