Analyze Time Series Data Using Markov Transition Fields

Time series data is of significant importance in fields like data science and control systems, where predictive data analysis is used to improve the performance of systems. This makes various numerical tools and techniques essential for analyzing time series data.

There are several methods to encode time series data in images. This allows the data to be processed in various frameworks, for example, neural networks. The Markov Transition Field (MTF) is one of the methods used to encode the data in a matrix in which the transition probability of a datapoint from one time step to another time step is encoded in matrix entries.

In this project, we will analyze the Electricity Transformer Temperature (ETT) by constructing its Markov transition field. We will start by constructing its adjacency matrix, and then we’ll evaluate its Markov matrix and Markov transition field. We will visualize the obtained matrices and observe the properties of the time series data revealed by the plots.