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Advanced forecasting with Python : with state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR By Joos Korstanje

By: Material type: TextTextPublication details: Apress, c2021.Edition: 1Description: i-xvii+296PISBN:
  • 9781484283400
Subject(s): DDC classification:
  • 1 005.133 KOR-A
Online resources:
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
Summary: :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,
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Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Dept. of Computational Biology and Bioinformatics 005.133 KOR-A (Browse shelf(Opens below)) Available DCB4167
Book Book Dept. of Futures Studies General Stacks Dept. of Futures Studies 005.133 KOR (Browse shelf(Opens below)) Available DFS4564

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

: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,

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