<ul><li>Time series forecasting is essential for predicting future values based on past temporal patterns in various domains like finance, energy, and healthcare.</li><li>AutoML platforms, such as those in Python, aim to streamline the machine learning process by automating tasks like data preprocessing, feature engineering, model selection, training, and hyperparameter tuning.</li><li>However, the critical question remains: Can AutoML effectively handle the complexities of complex time series data, including multivariate, irregular, noisy, seasonal, and non-stationary patterns?</li><li>The discussion around leveraging AutoML for time series forecasting goes beyond academic curiosity to the practical implications of democratizing this forecasting capability.</li></ul>

Is Python’s autoML capable of Handling complex Time series data?

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