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Advanced forecasting with Pythonmast...

Korstanje, Joos.

Advanced forecasting with Pythonmastering modern forecasting techniques with machine learning and cloud tools /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced forecasting with Pythonby Joos Korstanje.
Reminder of title:	mastering modern forecasting techniques with machine learning and cloud tools /
Author:	Korstanje, Joos.
Published:	Berkeley, CA :Apress :2025.
Description:	xix, 440 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Python (Computer program language)
Online resource:	https://doi.org/10.1007/979-8-8688-2028-1
ISBN:	9798868820281$q(electronic bk.)

Advanced forecasting with Pythonmastering modern forecasting techniques with machine learning and cloud tools /
Korstanje, Joos.

Advanced forecasting with Pythonmastering modern forecasting techniques with machine learning and cloud tools /[electronic resource] :by Joos Korstanje. - Second edition. - Berkeley, CA :Apress :2025. - xix, 440 p. :ill., digital ;24 cm.

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.

ISBN: 9798868820281$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-2028-1doiSubjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98 / K67 2025

Dewey Class. No.: 005.133

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505 0 $a 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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