https://github.com/gregoritsch3/markov_weather_model

A 5-State Markov Chain Weather Model whose transition probabilites are inferred from pre-existing daily weather data (https://www.kaggle.com/datasets/ananthr1/weather-prediction).
https://github.com/gregoritsch3/markov_weather_model

ergodic-stationary-processes jupyter-notebook markov-chain pandas prediction python

Last synced: 13 days ago
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A 5-State Markov Chain Weather Model whose transition probabilites are inferred from pre-existing daily weather data (https://www.kaggle.com/datasets/ananthr1/weather-prediction).

• Host: GitHub
• URL: https://github.com/gregoritsch3/markov_weather_model
• Owner: Gregoritsch3
• Created: 2024-10-16T11:59:09.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-02T16:06:39.000Z (15 days ago)
• Last Synced: 2024-11-02T17:18:11.744Z (15 days ago)
• Topics: ergodic-stationary-processes, jupyter-notebook, markov-chain, pandas, prediction, python
• Language: Jupyter Notebook
• Homepage:
• Size: 41 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
A 5-State Markov Chain Weather Model whose transition probabilites are inferred from pre-existing daily weather data (https://www.kaggle.com/datasets/ananthr1/weather-prediction) pertaining to Seattle, USA. Examples of state vectors at later steps are calculated, and the conditions for the Ergodic Theorem are examined. By numerical examination we conclude that the Ergodic Theorem does not apply, and there exist no stationary transition probabilites.