{"product_id":"advanced-forecasting-with-python-9781484271490","title":"Advanced Forecasting with Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eCover 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.\u003c\/p\u003e\u003cp\u003eRather 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. \u003c\/p\u003e\u003cp\u003eEach of the models presented in this book is covered in depth, with an intuitive simple explanation of th\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART I: Machine Learning for Forecasting\u003cbr\u003eChapter 1: Models for ForecastingChapter Goal: Explains the different categories of models that are relevant for forecasting in high level languageNo pages: 10Sub -Topics1.\tTime series models2.\tSupervised vs unsupervised models3.\tClassification vs regression models4.\tUnivariate vs multivariate models\u003cbr\u003eChapter 2: Model Evaluation for ForecastingChapter Goal: Explains model evaluation with specific adaptations to keep in mind for forecastingNo pages: 15Sub -Topics1.\tTrain test split2.\tCross validation for forecasting3.\tBacktesting\u003cbr\u003ePART II: Univariate Time Series Models\u003cbr\u003eChapter 3: The AR ModelChapter Goal: explain the AR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding AR model2.\tMathematical explanation of the AR model3.\tWorked out Python forecasting example with the AR model\u003cbr\u003eChapter 4: The MA modelChapter Goal: explain the MA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding MA model2.\tMathematical explanation of the MA model3.\tWorked out Python forecasting example with the MA model\u003cbr\u003eChapter 5: The ARMA modelChapter Goal: explain the ARMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding ARMA model2.\tMathematical explanation of the ARMA model3.\tWorked out Python forecasting example with the ARMA model\u003cbr\u003eChapter 6: The ARIMA modelChapter Goal: Explains the ARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding ARIMA model2.\tMathematical explanation of the ARIMA model3.\tWorked out Python forecasting example with the ARIMA model\u003cbr\u003eChapter 7: The SARIMA ModelChapter Goal: Explains the SARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding SARIMA model2.\tMathematical explanation of the SARIMA model3.\tWorked out Python forecasting example with the SARIMA model\u003cbr\u003ePART III: Multivariate Time Series Models\u003cbr\u003eChapter 8: The VAR modelChapter Goal: Explains the VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding VAR model2.\tMathematical explanation of the VAR model3.\tWorked out Python forecasting example with the VAR model\u003cbr\u003eChapter 9: The Bayesian VAR modelChapter Goal: Explains the Bayesian VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding Bayesian VAR model2.\tMathematical explanation of the Bayesian VAR model3.\tWorked out Python forecasting example with the Bayesian VAR model\u003cbr\u003ePART IV: Supervised Machine Learning Models\u003cbr\u003eChapter 10: The Linear Regression modelChapter Goal: Explains the Linear Regression model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding Linear Regression model2.\tMathematical explanation of the Linear Regression model3.\tWorked out Python forecasting example with the Linear Regression model\u003cbr\u003eChapter 11: The Decision Tree modelChapter Goal: Explains the Decision Tree model (intuitively, mathematically and give Python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding Decision Tree model2.\tMathematical explanation of the Decision Tree model3.\tWorked out Python forecasting example with the Decision Tree model\u003cbr\u003eChapter 12: The k-Nearest Neighbors VAR modelChapter Goal: explain the k-Nearest Neighbors (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding k-Nearest neighbors model2.\tMathematical explanation of the k-Nearest neighbors model3.\tWorked out Python forecasting example with the k-Nearest neighbors model\u003cbr\u003eChapter 13: The Random Forest ModelChapter Goal: explain the Random Forest (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1.\tUnderstanding Random Forest model2.\tMathematical explanation of the Random Forest model3.\tWorked out Python forecasting example with the Random Forest model\u003cbr\u003eChapter 14: The XGBoost modelChapter Goal: Explains the XGBoost model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1.\tUnderstanding XGBoost model2.\tMathematical explanation of the XGBoost model3.\tWorked out Python forecasting example with the XGBoost model\u003cbr\u003eChapter 15: The Neural Network modelChapter Goal: Explains the Neural Network model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1.\tUnderstanding Neural Network model2.\tMathematical explanation of the Neural Network model3.\tWorked out Python forecasting example with the Neural Network model\u003cbr\u003ePart V: Advanced Machine and Deep Learning Models\u003cbr\u003eChapter 16: Recurrent Neural NetworksChapter Goal: Explains Recurrent Neural Networks (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1.\tUnderstanding Recurrent Neural Networks2.\tMathematical explanation of Recurrent Neural Networks 3.\tWorked out Python forecasting example with Recurrent Neural Networks \u003cbr\u003eChapter 17: LSTMsChapter Goal: Explains LSTMs (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1.\tUnderstanding LSTMs2.\tMathematical explanation of LSTMs 3.\tWorked out Python forecasting example with LSTMs \u003cbr\u003eChapter 18: Facebook’s Prophet model\u003cbr\u003eChapter Goal: Explains Facebook’s Prophet model (intuitively, mathematically and give Python application with code and data set)No pages: 10Sub -Topics1.\tUnderstanding Facebook’s Prophet model2.\tMathematical explanation of Facebook’s Prophet model3.\tWorked out Python forecasting example with Facebook’s Prophet model\u003cbr\u003eChapter 19: Amazon’s DeepAR ModelChapter Goal: Explains Amazon’s DeepAR model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1.\tUnderstanding Amazon’s DeepAR model2.\tMathematical explanation of Amazon’s DeepAR model3.\tWorked out Python forecasting example with Amazon’s DeepAR model\u003cbr\u003eChapter 20: Deep State Space ModelsChapter Goal: Explains Deep State Space models (intuitively, mathematically and give Python application with code and data set)No pages: 10Sub -Topics1.\tUnderstanding Deep State Space models2.\tMathematical explanation of Deep State Space models3.\tWorked out Python forecasting example with Deep State Space models\u003cbr\u003eChapter 21: Model selectionChapter Goal: Give elements to select the best model for a specific situationNo pages: 16Sub -Topics1.\tBenchmark scores vs understandability of models vs compute time 2.\tBlack swan outlier problems3.\tAutomated retraining and updating of models4.\tConclusion\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default 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