import unittest
import importlib
import numpy as np
from statsmodels.tsa.arima_process import ArmaProcess

def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)

class TestARIMASimulation(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.user = importlib.import_module("user_code")
        np.random.seed(0)
        ar = np.array([1, -0.75, 0.25])
        ma = np.array([1, 0.65])
        cls.process = ArmaProcess(ar, ma)
        cls.generated = cls.process.generate_sample(nsample=500, scale=1.0)

    def test_data_length(self):
        cond = len(self.user.simulated_data) == 500
        _dynamic_test(self, cond, "Generated series has correct length (500).", "Generated series length incorrect.")

    def test_series_variability(self):
        cond = np.std(self.user.simulated_data) > 0.1
        _dynamic_test(self, cond, "Generated series shows variability.", "Generated series appears constant.")

    def test_arma_params(self):
        cond = np.allclose(self.user.ar, np.array([1, -0.75, 0.25])) and np.allclose(self.user.ma, np.array([1, 0.65]))
        _dynamic_test(self, cond, "AR and MA parameters correctly defined.", "AR or MA parameters incorrect.")

if __name__ == "__main__":
    unittest.main(argv=["first-arg-is-ignored"], exit=False)


test_main.py

Master the theory and practical implementation of ARIMA models for time series forecasting. This course guides you from foundational time series concepts through advanced ARIMA techniques, including hands-on coding and real-world forecasting challenges.

Explore the essential building blocks of time series data, including trend, seasonality, and stationarity, and learn how to visualize and interpret these patterns.

Delve into the mathematical principles behind ARIMA models, including autoregressive and moving average processes, and learn how to interpret ARIMA parameters.

Learn how to build, fit, and evaluate ARIMA models in Python, and understand key metrics for assessing forecast accuracy.

Explore advanced ARIMA variants, automated parameter selection, and strategies for comparing and selecting the best forecasting model.

Challenge: Simulating ARIMA Processes

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