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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Korstanje, Joos
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : Apress, 2021.
Temas:
Python (Computer program language)
Machine learning.
Time-series analysis > Data processing.
Python (Langage de programmation)
Apprentissage automatique.
Série chronologique > Informatique.
Machine learning
Time-series analysis > Data processing
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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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