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We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another aspect. The former features are dense on the time scale and latter features are sparse. Simply concatenate them into one feature vector seems not proper. Is there any typical method in machine learning can handle with problem ?

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Machine Learning does not have an specific method from mixing data with different sampling rates.

An extract from the paper:

We approach the problem of dealing with multiple sampling rates from an aggregation perspective. We propose Accordion, a new embedded method that constructs and selects aggregate features iteratively, in a memory-conscious fashion.

Our algorithms work on both classification and regression problems. We describe three experiments on real-world time series datasets, with satisfying results.

Mining Multivariate Time Series with Mixed Sampling Rates

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