Timeseries Data Classification in Python with Symbolic Aggregate Approx. & Vector Space Modeling

Описание: This is a course on Timeseries Data Classification. Timeseries data analysis using classification methods like bag of words approach called Symbolic Aggregate Approximation is discussed with Vector Space Modeling.
What is timeseries data, timeseries data analysis types and characteristics of timeseries data are discussed. Classifying labels on timeseries data using SAX-VSM method is described and the algo implementation is shown in a jupyter notebook demo in the end.

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Video Layout:
00:00:00 Introduction
00:48:00 Agenda
00:02:51 What is timeseries data
00:04:49 Timeseries data analysis
00:07:37 Timeseries data analysis types
00:09:45 Characteristics of timeseries data
00:12:39 Timeseries data classification
00:17:13 What is Shapelet transformation
00:18:43 Bag of words timeseries transformation
00:20:16 What is Symbolic Aggregate Approximation (SAX-VSM)
00:22:57 What is SAX-VSM algorithm
00:25:09 SAX-VSM algorithm implementation
00:26:56 What is Piecewise Aggregate Approximation (PAA)
00:30:39 Python SAX-VSM package
00:31:07 Discretization via sliding window
00:32:43 Discretization Numerosity Reduction in SAX-VSM
00:35:17 Dimensionality Reduction vs Numerosity Reduction
00:37:05 Vector Space Modelling (tf-idf)
00:39:55 tf-idf weighting scheme
00:41:24 What is Cosine Similarity
00:42:15 Cosine Similarity rules
00:43:13 Python jupyter notebook demo

All course material and jupyter notebook can be found here:
https://github.com/sultanadeel/timese...

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