Most of our machine learning algorithms are specifically tailored to extract patterns from time series data. Therefor, we need to deal with the time component of the data. A time series is a sequence of observations taken sequentially in time.
To handle the time component of the data, we have a powerful pipeline that can manipulate the data. We can forecast or predict future occurrences, extract time dependencies between the data, or smooth the signal when there is noise present.
Ekkono’s incremental learning algorithms handle streams of data, such as time series. Those and other batch trained algorithms can extract trends, seasonal patterns, and detect noisy data.
All of this is available both for standard CPUs and microcontrollers.
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