Filtering noisy data
Project description
microfilter
Some ad-hoc approaches to filtering noisy data that don't appear in textbooks
Usage example
Train filter on simulated noisy data
from microfilter.univariate.expnormdist import ExpNormDist
from microfilter.univariate.noisysim import sim_lagged_values_and_times
lagged_values, lagged_times = sim_lagged_values_and_times
dist = ExpNormDist()
dist.hyper_params['max_evals']=500
dist.fit(lagged_values=lagged_values, lagged_times=lagged_times)
pprint(dist.params)
new_value = 17.0
dist.update(value=new_value, dt=1.0)
pprint(dist.state)
See https://github.com/microprediction/microfilter/blob/master/examples/plot_expnorm.py
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