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Subtract that constant from each observation in [t - horizon + 1 : t]
Take the root-mean-square of that timeseries of deviations from the constant mean
What this INSTEAD DOES at time t is
Estimate mean over [t - horizon - horizon + 1 : t - horizon + 1]
Subtract that from the single observation at t - horizon + 1
Estimate mean over [t - horizon - horizon + 2 : t - horizon + 2]
Subtract that from the single observation at t - horizon + 2
...
THEN finally take the root-mean-square of that timeseries of deviations from the time-varying (because it looks backward over horizon timesteps at each timestep) mean!
What this SHOULD DO at time
t
is[t - horizon + 1 : t]
[t - horizon + 1 : t]
What this INSTEAD DOES at time
t
is[t - horizon - horizon + 1 : t - horizon + 1]
t - horizon + 1
[t - horizon - horizon + 2 : t - horizon + 2]
t - horizon + 2
horizon
timesteps at each timestep) mean!Shit.
Refer to:
https://github.com/sparshsah/foggy-lib/blob/0648d816a910fcdea4a654265f50a5578f5afd23/util/foggy_pylib/stats/est/tsa.py#L179
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