CODEWITHSUNDEEP
machine-learningfeature-extraction
Chris vCB
Chris vCB

Reputation: 1053

What algorithms can be used to build predictors from fractions of time series?

Let S and T be sets of time series labelled with a property. Each time series is highly periodic and in fact contains subsequent repeats of the same process (consider e.g. a gait recording, which is a time series of foot positions that repeat the same motion, which I'm calling a segment for simplicity's sake).

What is a good feature extractor if my objective is to build a model that from a sequence of such segments returns a similarity score to S or T? Ignore the model itself for now - just consider feature extraction for the time being,

Upvotes: 1

Views: 48

Answers (1)

greeness
greeness

Reputation: 16114

What you described falls into below problem:

  • Given a sequence features.
  • Classify or recognize the hidden state.

For example, in machine-vision, the sequence could be images captured continuously against a moving human. The goal is to identify certain categories of gestures.

In your problem, the input is d-dimensional time series data and your output is the probability of two classes (S and T).

There are some general methods to handle such problem, namely, hidden markov model (HMM) and conditional random fields (CRF).

Upvotes: 1

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