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210803 ||| eng |
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|a 9781484271506
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| 100 |
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|a Korstanje, Joos
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| 245 |
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|a Advanced Forecasting with Python
|h Elektronische Ressource
|b With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR
|c by Joos Korstanje
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| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Berkeley, CA
|b Apress
|c 2021, 2021
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| 300 |
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|a XVII, 296 p. 106 illus., 36 illus. in color
|b online resource
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| 505 |
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|a Chapter 1: Models for Forecasting -- Chapter 2: Model Evaluation for Forecasting -- Chapter 3: The AR Model -- Chapter 4: The MA model -- Chapter 5: The ARMA model -- Chapter 6: The ARIMA model -- Chapter 7: The SARIMA Model -- Chapter 8: The VAR model -- Chapter 9: The Bayesian VAR model -- Chapter 10: The Linear Regression model -- Chapter 11: The Decision Tree model -- Chapter 12: The k-Nearest Neighbors VAR model -- Chapter 13: The Random Forest Model -- Chapter 14: The XGBoost model -- Chapter 15: The Neural Network model -- Chapter 16: Recurrent Neural Networks -- Chapter 17: LSTMs -- Chapter 18: Facebook’s Prophet model -- Chapter 19: Amazon’s DeepAR Model -- Chapter 20: Deep State Space Models -- Chapter 21: Model selection
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| 653 |
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|a Machine learning
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| 653 |
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|a Machine Learning
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| 653 |
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|a Python
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| 653 |
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|a Python (Computer program language)
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
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|a 10.1007/978-1-4842-7150-6
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| 856 |
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|u https://doi.org/10.1007/978-1-4842-7150-6?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.31
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| 520 |
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|a Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, amathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. You will: Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case
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