https://github.com/stevenrice99/llm-forecast

A Novel Hybridized Forecasting Technique Utilizing ARIMA and Large Language Models
https://github.com/stevenrice99/llm-forecast

arima arima-forecasting arima-model arima-modeling forecast forecasting forecasting-model forecasting-models large-language-model large-language-models llama llama3-1 llm prediction-model predictive-analytics predictive-modeling prompt-engineering python python3 statsmodels

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A Novel Hybridized Forecasting Technique Utilizing ARIMA and Large Language Models

• Host: GitHub
• URL: https://github.com/stevenrice99/llm-forecast
• Owner: StevenRice99
• License: mit
• Created: 2024-06-20T16:13:53.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-12-20T20:32:36.000Z (10 months ago)
• Last Synced: 2025-01-16T10:40:34.916Z (9 months ago)
• Topics: arima, arima-forecasting, arima-model, arima-modeling, forecast, forecasting, forecasting-model, forecasting-models, large-language-model, large-language-models, llama, llama3-1, llm, prediction-model, predictive-analytics, predictive-modeling, prompt-engineering, python, python3, statsmodels
• Language: Python
• Homepage: https://stevenrice.ca#llm-forecast
• Size: 123 MB
• Stars: 1
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

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README

          
# A Novel Hybridized Forecasting Technique Utilizing ARIMA and Large Language Models

## Usage

1. This project requires [Ollama](https://ollama.com "Ollama"), so ensure it is installed and running.

2. For web scraping, you need to have [Mozilla Firefox](https://www.mozilla.org/en-CA/firefox "Mozilla Firefox") installed.

3. Install requirements by running ``pip install -r requirements.txt`` in the directory of this project. It is recommended you do this in a [virtual environment](https://docs.python.org/3/tutorial/venv.html "Python Virtual Environments and Packages").

4. To replicate our tests, first delete the ``Data``, ``Results``, and ``Responses`` folders.

5. Run ``main.py`` to perform the experiments. Sometimes, during the web scraping [Selenium](https://www.selenium.dev "Selenium") using [Mozilla Firefox](https://www.mozilla.org/en-CA/firefox "Mozilla Firefox") may hang, and not move onto the next news article. In this case, simply restart the script, and it will continue from where it left off.

   1. ``-f`` or ``--forecast`` - Number of weeks to forecast. Defaults to twelve.

   2. ``-w`` or ``--width`` - The width of the figures. Defaults to eight.

   3. ``-t`` or ``--height`` - The height of the figures. Defaults to 3.45.

   4. ``-d`` or ``--decimals`` - The number of decimal spaces. Defaults to two.

   5. ``-a`` or ``--alpha`` - The alpha factor for the desired confidence level which by default is 95%.

   6. ``-c`` or ``--clamp`` - By how much should forecast values be clamped around the baseline prediction. Defaults to one hundred.

   7. ``-l`` or ``--latest`` - Up to how many latest weeks of data should we keep. Defaults to zero meaning keep all data.

6. Under the ``Data`` folder, you will see the summaries of the news articles produced by the large language model. Under the ``Results`` folder, you will see the results charts and plots.

   1. ``Actual.csv`` - The actual COVID-19 hospitalizations to occur over the next given weeks from a given week.

   2. ``Baseline.csv``, ``Unmasked.csv``, and ``Masked.csv`` - The baseline model and full model predictions of how many COVID-19 hospitalizations to occur over the next given weeks from a given week.

   3. ``Difference Baseline.csv`` and ``Difference Full Model.csv`` - The difference between the actual results and each of the model results.

   4. ``Success Rate.csv`` - The success rate of each model, where success was determined if a forecast met or exceeded the actual amounts of hospitalizations to occur over a period.

   5. ``Average Difference.csv`` - The average difference each model had from the actual amounts of hospitalizations to occur over a given period.

   6. ``Total Failures.csv`` - The total failures which occurred for each model over a forecasting period.

   7. ``Total Excess.csv`` - The total excess which occurred for each model over a forecasting period.

   8. ``WIS.csv`` - The weighted interval scores.

   9. ``MAE.csv`` - The mean absolute errors.

   10. ``Coverage.csv`` - Coverage of values falling within the alpha prediction intervals.

7. ``Terms.txt`` included all terms for COVID-19 which should be masked.

8. ``Trusted.txt`` includes all trusted publishers.

   1. To list all untrusted publishers in all the summarized news articles, run ``publishers.py``. Passing in either ``-t`` or ``--trusted`` will list all trusted publishers in all the summarized news articles.

## Data

The data used for this experiment is taken from [Covid Timeline Canada](https://github.com/ccodwg/CovidTimelineCanada/blob/main/data/pt/hosp_admissions_pt.csv "Covid Timeline Canada GitHub").