Added ARIMA #13444
Added ARIMA #13444
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algorithms-keeper bot added require descriptive names ![algorithms-keeper[bot]](https://avatars.githubusercontent.com/in/235834?s=60&v=4){kind=small}
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| class ARIMA: | ||
| def __init__(self, p=1, d=1, q=1, lr=0.001, epochs=1000) -> None: |
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Please provide type hint for the parameter: p
Please provide descriptive name for the parameter: p
Please provide type hint for the parameter: d
Please provide descriptive name for the parameter: d
Please provide type hint for the parameter: q
Please provide descriptive name for the parameter: q
Please provide type hint for the parameter: lr
Please provide type hint for the parameter: epochs
| self.n_train: int | None = None | ||
| self.sigma_err: float | None = None | ||
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| def difference(self, data) -> NDArray[np.float64]: |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/arima.py, please provide doctest for the function difference
Please provide type hint for the parameter: data
| diff = np.diff(diff) # np.diff is a handy function that does exactly this. | ||
| return diff | ||
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| def inverse_difference( |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/arima.py, please provide doctest for the function inverse_difference
| prev = next_val | ||
| return forecast | ||
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| def _compute_residuals( |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/arima.py, please provide doctest for the function _compute_residuals
| diff_data: NDArray[np.float64], | ||
| phi: NDArray[np.float64], | ||
| theta: NDArray[np.float64], | ||
| c: float, |
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Please provide descriptive name for the parameter: c
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| return preds, errors | ||
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| def fit(self, data: list[float] | NDArray[np.float64]) -> "ARIMA": |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/arima.py, please provide doctest for the function fit
| self.n_train = len(diff_data) # Assign n_train as an integer | ||
| return self | ||
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| def _fit_gradient_descent(self, diff_data: NDArray[np.float64], start: int) -> None: |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/arima.py, please provide doctest for the function _fit_gradient_descent
| msg = f"Fitted params (GD): phi={self.phi},theta={self.theta},c={self.c:.6f}\n" | ||
| print(msg) | ||
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| def forecast( |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/arima.py, please provide doctest for the function forecast
1 participant
Describe your change:
Added the ARIMA Algorithm.
Checklist: