Do I need to engineer lagged features when creating an LSTM for time series forecasting?
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Long short-term memory networks are fairly complicated and I haven't completely wrapped my head around them.
It seems to me like the big gain in LSTMs for time series forecasting is the lacking necessity for lagged features: it determines on its own which lagged information is actually significant and remembers it for the next tiestep(s).
Should one still create lagged features as inputs for timesteps when training an LSTM? Like the output of the last timestep, or means and medians of the foregoing timesteps, means and medians for a specific class, distances, differences, etc.?
neural-network time-series lstm rnn feature-engineering
asked 5 hours ago
DennyDenny
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Long short-term memory networks are fairly complicated and I haven't completely wrapped my head around them.
It seems to me like the big gain in LSTMs for time series forecasting is the lacking necessity for lagged features: it determines on its own which lagged information is actually significant and remembers it for the next tiestep(s).
Should one still create lagged features as inputs for timesteps when training an LSTM? Like the output of the last timestep, or means and medians of the foregoing timesteps, means and medians for a specific class, distances, differences, etc.?
neural-network time-series lstm rnn feature-engineering
asked 5 hours ago
DennyDenny
61
New contributor
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Long short-term memory networks are fairly complicated and I haven't completely wrapped my head around them.
It seems to me like the big gain in LSTMs for time series forecasting is the lacking necessity for lagged features: it determines on its own which lagged information is actually significant and remembers it for the next tiestep(s).
Should one still create lagged features as inputs for timesteps when training an LSTM? Like the output of the last timestep, or means and medians of the foregoing timesteps, means and medians for a specific class, distances, differences, etc.?
neural-network time-series lstm rnn feature-engineering
asked 5 hours ago
DennyDenny
61
New contributor
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Long short-term memory networks are fairly complicated and I haven't completely wrapped my head around them.
It seems to me like the big gain in LSTMs for time series forecasting is the lacking necessity for lagged features: it determines on its own which lagged information is actually significant and remembers it for the next tiestep(s).
Should one still create lagged features as inputs for timesteps when training an LSTM? Like the output of the last timestep, or means and medians of the foregoing timesteps, means and medians for a specific class, distances, differences, etc.?
neural-network time-series lstm rnn feature-engineering
neural-network time-series lstm rnn feature-engineering
asked 5 hours ago
DennyDenny
61
New contributor
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 hours ago
DennyDenny
61
New contributor
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 hours ago
DennyDenny
61
New contributor
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 hours ago
DennyDenny
61
asked 5 hours ago
DennyDenny
61
61
New contributor
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Denny is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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