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This project uses an AutoRegressive Integrated Moving Average (ARIMA) model to predict future numbers in a sequence based on a historical dataset. The model is trained on a dataset of six sequences of numbers, and it predicts the next three numbers in each sequence.

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arianacason/ARIMA-TimeSeriesPredictive-Modeling

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ARIMA-TimeSeriesPredictive-Modeling

This project uses an AutoRegressive Integrated Moving Average (ARIMA) model to predict future numbers in a sequence based on a historical dataset. The model is trained on a dataset of six sequences of numbers, and it predicts the next three numbers in each sequence.

How to Run

Ensure that you have Python 3 installed, along with the necessary libraries: pandas, statsmodels, and sklearn. You can install these libraries using pip: "pip install pandas statsmodels scikit-learn"

Clone this repository to your local machine: "git clone https://github.com/yourusername/ARIMA-Number-Prediction.git"

Navigate to the directory containing the project: "cd ARIMA-Number-Prediction"

Run the script with Python: "python .py"

Model The model used in this project is an ARIMA model with parameters (5,1,0). These parameters were chosen based on preliminary analysis of the data, and they may need to be adjusted for different datasets.

About

This project uses an AutoRegressive Integrated Moving Average (ARIMA) model to predict future numbers in a sequence based on a historical dataset. The model is trained on a dataset of six sequences of numbers, and it predicts the next three numbers in each sequence.

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