Abstract
TSML (Time Series Machine Learning)
Highlights
TSML[5] is a Julia[1] package for time series data processing, classification, and prediction
The TSML package assumes a two-column input for any time series data composed of dates and values
The first part of the workflow aggregates values based on the specified date/time interval which minimizes occurrence of missing values and noise
Summary
TSML[5] is a Julia[1] package for time series data processing, classification, and prediction. Remaining missing values are replaced by the median/mean or user-defined aggregation function of the k-nearest neighbors (k-NN) where k is the symmetric distance from the location of missing value. This approach can be called several times until there are no more missing values
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
