Advanced Forecasting with Python With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR
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 mo...
Main Author: | |
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Format: | eBook |
Language: | English |
Published: |
Berkeley, CA
Apress
2021, 2021
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Edition: | 1st ed. 2021 |
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Online Access: | |
Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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