Weather Forecasting Model Implementation Using MATLAB

In this project, we implement a weather forecasting model using MATLAB. The model leverages the concept of Markov processes and historical weather data to predict future weather conditions. The implementation begins with collecting substantial historical weather data followed by preprocessing and cleaning operations using MATLAB's data handling functions like readtable and rmmissing to ensure data accuracy and reliability. We then employ MATLAB's statistical tools and machine learning algorithms, particularly the Markov chain functions from the Statistics and Machine Learning Toolbox, to build the predictive model. The model incorporates probability transition relationships between different weather states and analyzes their temporal evolution patterns. Key implementation aspects include calculating transition probability matrices using hmmestimate and generating predictions with hmmgenerate. Finally, we demonstrate the model's application in forecasting various weather parameters such as temperature, humidity, and precipitation levels through visualization techniques like plot and heatmap functions.