Hello!
Im analysing signal and need to evaluate window of 2 000 000 ticks.
Object also has "silence" phase when there is no activity but some noises.
Watching to this huge window human can recognize object behaviour
changing related to "silence".
So I have an idea to train system to predict "silence", then I would
stop training and start watching for 2 000 000 window to recognize anomaly.
Is hotgym good example to use? How to set up better MetricSpec() and
model_param.py in terms of described problem.
MetricSpec(field='P0', metric='multiStep',
inferenceElement='multiStepBestPredictions',
params={'errorMetric': 'altMAPE', 'window': 2000000,
'steps': 5})
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