Usual classifier time encoding

[Sandy4321]

Great attempt for rnns
But would be for use of
Usual classifier
Did you tried his concatenate data in sliding time window

[crazyleg]

I don't get the question and therefor can't give you an answer

[Sandy4321]

pls see this
4. Create Lag Features:
https://mlwhiz.com/blog/2017/12/26/win_a_data_science_competition/
or
https://machinelearningmastery.com/basic-feature-engineering-time-series-data-python/

The addition of lag features is called the sliding window method, in this case with a window width of 1. It is as though we are sliding our focus along the time series for each observation with an interest in only what is within the window width.

We can expand the window width and include more lagged features. For example, below is the above case modified to include the last 3 observed values to predict the value at the next time step
from pandas import DataFrame
from pandas import concat
series = read_csv('daily-min-temperatures.csv', header=0, index_col=0)
temps = DataFrame(series.values)
dataframe = concat([temps.shift(3), temps.shift(2), temps.shift(1), temps], axis=1)
dataframe.columns = ['t-3', 't-2', 't-1', 't+1']
print(dataframe.head(5))

or
https://www.mikulskibartosz.name/forecasting-time-series-using-lag-features/

so you just concatenate all features for each time stamp