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PROJECT


Analyze Time Series Data Using Markov Transition Fields

In this project, we will analyze time series data using the Markov transition field to observe its temporal properties. We will implement the project stepwise. These steps include the construction of the Markov matrix, the construction of the Markov transition field, and the extraction of valuable properties.

Analyze Time Series Data Using Markov Transition Fields

You will learn to:

Plot data using dedicated Python libraries.

Extract useful information from a time series dataset.

Create and analyze a Markov transition field.

Extract and process data using dedicated tools and techniques.

Skills

Data Plotting

Data Statistics

Data Visualization

Prerequisites

Basic understanding of Linear Algebra

Experience with Python

Technologies

Pyts

NumPy

Pandas

Matplotlib

Scikit-image

Project Description

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.

Project Tasks

1

Construct the Markov Transition Matrix

Task 1: Import Required Libraries and Packages

Task 2: Import the Time Series Dataset

Task 3: Truncate and Plot the DataFrame

Task 4: Discretize the Data

Task 5: Create the Adjacency Matrix

Task 6: Calculate the Markov Matrix

Task 7: Create the Markov Transition Field

2

Analyze the Markov Transition Field

Task 8: Visualize the Markov Transition Field

Task 9: Downsample the Markov Transition Field

Task 10: Plot Self-Transition Probabilities on Time Series Data

Congratulations!