STEPS:

1. Import Required Libraries and Packages:

Import necessary libraries and packages for data analysis, visualization, and modeling.

2.Import the Time Series Dataset:

Load the time series dataset(ETTh1 (1).csv) containing 17420 data with timestamp(date) and OilTemperature(OT).

3.Truncate and Plot the DataFrame:

Preprocess the dataset by truncating if needed and visualize the time series data through plots.

4.Discretize the Data:

Discretize the continuous temperature data to convert it into a format suitable for Markov analysis.

5.Create the Adjacency Matrix:

Construct the adjacency matrix representing the relationships between discretized temperature values.

6.Calculate the Markov Matrix:

Calculate the Markov matrix based on transition probabilities between different temperature states.

7.Create the Markov Transition Field:

Utilize the Markov matrix to create a Markov Transition Field, which visually represents the transition probabilities over time.

8.Visualize the Markov Transition Field:

Visualize the Markov Transition Field to observe patterns and properties in the temperature time series data.

9.Downsample the Markov Transition Field:

Optionally downsample the Markov Transition Field for better visualization or computational efficiency.

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

Plot self-transition probabilities on the original time series data to gain insights into temporal dependencies.

11.ARIMA Model for Forecasting:

Utilize the ARIMA model for forecasting future temperature values based on historical data.

12.Evaluate Model Performance:

Assess the accuracy of the ARIMA model by calculating metrics such as Root Mean Squared Error (RMSE).