I have to handle a huge amount of samples, where each sample contains unique time series. The goal is to feed this data into the Tensorflow LSTM model and predict some features. I have created the `tf timeseries_dataset_from_array`

generator function to feed the data to the TF model, but I haven’t figured out how to create a generator function when I have multiple samples. If I use the usual pipeline, `tf timeseries_dataset_from_array`

overlap the time series of two individual samples.

Does anyone have an idea how to effectively pass a time series of multiple samples to the TF model?

E.g. the Human Activity Recognition Dataset is one such dataset where each person has a separate long, time series, and each user’s time series can be further parsed with the SLIDING/ROLLING WINDOS-like `timeseries_dataset_from_array`

function.

Here is a simpler example:

I want to use `timeseries_dataset_from_array`

to generate samples for the TF model. Example: sample 1 where column 0 has 0, sample 2 starts where column 0 has 100. Here is a simpler example:

I want to get 3D data (samples, timesteps, features) without overlap.For example (6,2,7) Like this:

Here is the sample code:

```
from tensorflow.keras.preprocessing import timeseries_dataset_from_array
import numpy as np
x = np.array([[0,1,2,3,4,5,6],
[0,11,12,13,14,15,16],
[0,21,22,23,24,25,26],
[0,31,32,33,34,35,36],
[0,41,42,43,44,45,46]
])
xx = np.concatenate((x, x+100), axis=0)#.reshape(2,5,6)
sequence_length=2
stride=1
rate=1
input_dataset = timeseries_dataset_from_array(xx,
None,
sequence_length,
sequence_stride=stride,
sampling_rate=rate)
x_test = np.concatenate([x for x in input_dataset], axis=0)
```