000			01650nam a22001817a 4500
020			_a9781484283400
082			_21 _a005.133 _bKOR-A
100			_aKorstanje, Joos
245			_aAdvanced forecasting with Python : with state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR _cBy Joos Korstanje
250			_a1
260			_bApress, _cc2021.
300			_ai-xvii+296P.
505			_aChapter 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
520			_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,
650			_a Electronic books, Machine learning Python, Time-series analysis Data processing
856			_uhttps://www.worldcat.org/title/1259625412
942			_cBK
999			_c661986 _d661986