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As always in ML modelling problems: it depends. The critical factor here is that you are predicting based on properties of a sequence. The sequence does not need to be sampled at fixed time steps, or even be time based at all. E.g. in natural language processing the sequence of letters or words is only very loosely associated with the timing of the same ...


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from keras.preprocessing import Tokenizer samples = ["grss is green and sun is hot"] tokenizer = Tokenizer(num_words=1000) tokenizer.fit_on_texts(samples) sequences = tokenizer.texts_to_sequences(samples) The Keras library uses it's tokenizer function but you have other well known libraries like nltk, gensim to convert them into sequences and pass it into ...


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