Pattern recognition for temporal data

1. Which type of data can be accepted in pattern recognition?
2. How do you identify patterns in time series data?
3. What are the three main models of pattern recognition?

Which type of data can be accepted in pattern recognition?

Pattern recognition is a derivative of machine learning that uses data analysis to recognize incoming patterns and regularities. This data can be anything from text and images to sounds or other definable qualities. The technique can quickly and accurately recognize partially hidden patterns even in unfamiliar objects.

How do you identify patterns in time series data?

Many methods that recognize patterns in time series do so by first transforming the time series to a more common type of data. Then a classical machine learning algorithm is used to detect and classify the pattern. Visual pattern recognition achieves this by first transforming the data into a picture.

What are the three main models of pattern recognition?

There are six main theories of pattern recognition: template matching, prototype-matching, feature analysis, recognition-by-components theory, bottom-up and top-down processing, and Fourier analysis.