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I am wanting to use an LSTM for anomaly detection on a multivariate time series data. Let's say there are n rows each corresponding to a timestamp incrementing by an hour and d input features and d output flags for each feature. These flags are 0,1 or 2. I know how to shape the input data (samples, timesteps, features. So If I take m timesteps in a sample, when I give a 2D input as (1,m,d), I want the output to be the matrix m*d (which is the d output flags for each timestep across m timesteps in the sample.

  1. How do I shape the output data and layers of my NN for this ?

I have tried using keras.preprocessing.timeseries_dataset_from_array. However, for an input of (batch_size, timesteps, input features) it gives as output data (batch_size, output features) instead of (batchsize, timesteps, outpupt features) I would be glad to give additional information as needed and would greatly appreciate any help. Thanks and regards

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