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Book Synopsis

PART I: Machine Learning for Forecasting.- Chapter 1: Models for Forecasting.- Chapter 2: Model Evaluation for Forecasting.- Chapter 3: Model Management and Benchmarking using MLflow.- PART II: Univariate Time Series Models.- Chapter 4: The AR model.- Chapter 5: The MA model.- Chapter 6: The ARMA model.- Chapter 7: The ARIMA model.- Chapter 8: The SARIMA model.- PART III: Multivariate Time Series Models.- Chapter 9: The SARIMAX model.- Chapter 10: The VAR model.- Chapter 11: The VARMAX model.- PART IV: Supervised Models.- Chapter 12: The Linear Regression.- Chapter 13: The Decision Tree Model.- Chapter 14: The kNN model.- Chapter 15: The Random Forest.- Chapter 16: Gradient Boosting with XGBoost, LightGBM, and CatBoost.- Chapter 17: Bayesian Models with pyBATS.- PART V: Neural Networks.- Chapter 18: Neural Networks.- Chapter 19: RNNs using SimpleRNN and GRU.- Chapter 20: LSTM RNNs.- PART VI: Black Box and Cloud Based Models.- Chapter 21: The NBEATS model with Darts.- Chapter 22: The Transformer model with Darts.- Chapter 23: The NeuralProphet model.- Chapter 24: The DeepAR model and AWS Sagemaker AI.- Chapter 25: Uber's Orbit Model.- Chapter 26: AutoML with Microsoft Azure.- Chapter 27: AutoML with Vertex AI on Google Cloud Platform.- Chapter 28: Nixtla Suite and TimeGPT.- Chapter 29: Model Selection.

Advanced Forecasting with Python

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    A Paperback by Joos Korstanje

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      View other formats and editions of Advanced Forecasting with Python by Joos Korstanje

      Publisher: Apress
      Publication Date: 11/02/2026
      ISBN13: 9798868820274, 979-8868820274
      ISBN10:
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis

      PART I: Machine Learning for Forecasting.- Chapter 1: Models for Forecasting.- Chapter 2: Model Evaluation for Forecasting.- Chapter 3: Model Management and Benchmarking using MLflow.- PART II: Univariate Time Series Models.- Chapter 4: The AR model.- Chapter 5: The MA model.- Chapter 6: The ARMA model.- Chapter 7: The ARIMA model.- Chapter 8: The SARIMA model.- PART III: Multivariate Time Series Models.- Chapter 9: The SARIMAX model.- Chapter 10: The VAR model.- Chapter 11: The VARMAX model.- PART IV: Supervised Models.- Chapter 12: The Linear Regression.- Chapter 13: The Decision Tree Model.- Chapter 14: The kNN model.- Chapter 15: The Random Forest.- Chapter 16: Gradient Boosting with XGBoost, LightGBM, and CatBoost.- Chapter 17: Bayesian Models with pyBATS.- PART V: Neural Networks.- Chapter 18: Neural Networks.- Chapter 19: RNNs using SimpleRNN and GRU.- Chapter 20: LSTM RNNs.- PART VI: Black Box and Cloud Based Models.- Chapter 21: The NBEATS model with Darts.- Chapter 22: The Transformer model with Darts.- Chapter 23: The NeuralProphet model.- Chapter 24: The DeepAR model and AWS Sagemaker AI.- Chapter 25: Uber's Orbit Model.- Chapter 26: AutoML with Microsoft Azure.- Chapter 27: AutoML with Vertex AI on Google Cloud Platform.- Chapter 28: Nixtla Suite and TimeGPT.- Chapter 29: Model Selection.

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