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Usually, timeseries data goes through a 'normalization' process, the purpose of which is to take non-uniform, and possibly multiple datasets and produce
a single uniform timeseries which can be analyzed a little more easily in most cases. We copy here the list of normalization steps that are currently
performed within the C3 platform, these are available from C3's official documentation here: https://developer.c3.ai/docs/7.12.0/guide/guide-c3aisuite-basic/ts-normalization-engine
- Drop data points with irregular dates. For example, dates where start date is after end date, dates are > 50 years apart, etc.
- Remove duplicate data points that might have been sent due to data loading issues or issues with IoT sensor hardware.
- Correctly apportion the values in the correct time interval in case of overlapping data points.
- Convert data points in various units into a homogenous unit utilizing C3's unit conversion capabilities.
- Automatic detection of the natural frequency of the data.
- Aggregate or disaggregate data into coarse or finer intervals to optimize for storage and accuracy.
Once the normalization process is complete, a single time series sampled at a uniform interval is given.
SimpleMetrics
CompoundMetrics
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