Dr ben auffarth (7 resultados)

Condición: New.

Condición: New.

Paperback. Condición: new. Paperback. Get better insights from time-series data and become proficient in building models with real-world dataKey FeaturesExplore time series forecasting and time series analysis in Python using ARIMA, SARIMA, GARCH, gradient boosting, and recurrent neural networks.Improve predictive modeling with feature engineering and forecasting machine learning techniques.Apply demand forecasting and financial forecasting methods through practical case studies and real-world datasets.Book DescriptionMachine Learning for Time Series, Second Edition offers a practical guide to building forecasting systems with the most important libraries such as sktime, darts, Nixtla's skforecast and mlforecast, and AutoGluon among others. Starting with the fundamentals, youll learn how to characterize datasets, measure and validate prediction performance, engineer features, and build forecasting pipelines.The book covers classical methods such as ARIMA and Theta, machine learning methods such as gradient boosting, and deep learning with recurrent, latest Transformer-based (like Crossformer), or convolutional (TimesNet) models. We'll apply foundation models such as TimesFM, Chronos, and OpenTSLM, or generative approaches like Chain-of-Charts, and uncertainty quantification with conformal prediction such as GARCH conformal, conformalized quantile regression, or split conformal. Well also dive into production infrastructure like feature stores, model tracking, and monitoring and scaling strategies, as well as classification, self-supervised encoding, spatiotemporal modeling, handling irregular time series with ODEs, survival analysis, and causal inference.Through practical examples, youll learn how to evaluate models, detect and handle anomalies and drift, and choose approaches that match data and operational needs.What you will learnApply classical time series models such as ETS, ARIMA, and ThetaValidate with SMAPE, MASE, RMSSE and rolling window, and walk-forwardForecast as regression with sktime, darts, mlforecast, and skforecastUse modern deep learning like LSTMs, DLinear, TSMixer, and TimesNetGrasp foundation models such as TimesFM, OpenTSLM, or Chain-of-ChartsQuantify uncertainty quantification with conformalized regressionSet up feature stores, MLflow, and monitoring and scaling strategiesHandle classification, drift, spatiotemporal and irregular time seriesWho this book is forThis book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book. Gain practical skills in time series forecasting, time series analysis, and predictive modeling with Python. Learn ARIMA, SARIMA, gradient boosting, recurrent neural networks, and feature engineering through real-world forecasting examples. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. Get better insights from time-series data and become proficient in building models with real-world dataKey FeaturesExplore time series forecasting and time series analysis in Python using ARIMA, SARIMA, GARCH, gradient boosting, and recurrent neural networks.Improve predictive modeling with feature engineering and forecasting machine learning techniques.Apply demand forecasting and financial forecasting methods through practical case studies and real-world datasets.Book DescriptionMachine Learning for Time Series, Second Edition offers a practical guide to building forecasting systems with the most important libraries such as sktime, darts, Nixtla's skforecast and mlforecast, and AutoGluon among others. Starting with the fundamentals, youll learn how to characterize datasets, measure and validate prediction performance, engineer features, and build forecasting pipelines.The book covers classical methods such as ARIMA and Theta, machine learning methods such as gradient boosting, and deep learning with recurrent, latest Transformer-based (like Crossformer), or convolutional (TimesNet) models. We'll apply foundation models such as TimesFM, Chronos, and OpenTSLM, or generative approaches like Chain-of-Charts, and uncertainty quantification with conformal prediction such as GARCH conformal, conformalized quantile regression, or split conformal. Well also dive into production infrastructure like feature stores, model tracking, and monitoring and scaling strategies, as well as classification, self-supervised encoding, spatiotemporal modeling, handling irregular time series with ODEs, survival analysis, and causal inference.Through practical examples, youll learn how to evaluate models, detect and handle anomalies and drift, and choose approaches that match data and operational needs.What you will learnApply classical time series models such as ETS, ARIMA, and ThetaValidate with SMAPE, MASE, RMSSE and rolling window, and walk-forwardForecast as regression with sktime, darts, mlforecast, and skforecastUse modern deep learning like LSTMs, DLinear, TSMixer, and TimesNetGrasp foundation models such as TimesFM, OpenTSLM, or Chain-of-ChartsQuantify uncertainty quantification with conformalized regressionSet up feature stores, MLflow, and monitoring and scaling strategiesHandle classification, drift, spatiotemporal and irregular time seriesWho this book is forThis book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book. Gain practical skills in time series forecasting, time series analysis, and predictive modeling with Python. Learn ARIMA, SARIMA, gradient boosting, recurrent neural networks, and feature engineering through real-world forecasting examples. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Get better insights from time-series data and become proficient in building models with real-world dataKey FeaturesExplore time series forecasting and time series analysis in Python using ARIMA, SARIMA, GARCH, gradient boosting, and recurrent neural networks.Improve predictive modeling with feature engineering and forecasting machine learning techniques.Apply demand forecasting and financial forecasting methods through practical case studies and real-world datasets.Book DescriptionMachine Learning for Time Series, Second Edition offers a practical guide to building forecasting systems with the most important libraries such as sktime, darts, Nixtla's skforecast and mlforecast, and AutoGluon among others. Starting with the fundamentals, youll learn how to characterize datasets, measure and validate prediction performance, engineer features, and build forecasting pipelines.The book covers classical methods such as ARIMA and Theta, machine learning methods such as gradient boosting, and deep learning with recurrent, latest Transformer-based (like Crossformer), or convolutional (TimesNet) models. We'll apply foundation models such as TimesFM, Chronos, and OpenTSLM, or generative approaches like Chain-of-Charts, and uncertainty quantification with conformal prediction such as GARCH conformal, conformalized quantile regression, or split conformal. Well also dive into production infrastructure like feature stores, model tracking, and monitoring and scaling strategies, as well as classification, self-supervised encoding, spatiotemporal modeling, handling irregular time series with ODEs, survival analysis, and causal inference.Through practical examples, youll learn how to evaluate models, detect and handle anomalies and drift, and choose approaches that match data and operational needs.What you will learnApply classical time series models such as ETS, ARIMA, and ThetaValidate with SMAPE, MASE, RMSSE and rolling window, and walk-forwardForecast as regression with sktime, darts, mlforecast, and skforecastUse modern deep learning like LSTMs, DLinear, TSMixer, and TimesNetGrasp foundation models such as TimesFM, OpenTSLM, or Chain-of-ChartsQuantify uncertainty quantification with conformalized regressionSet up feature stores, MLflow, and monitoring and scaling strategiesHandle classification, drift, spatiotemporal and irregular time seriesWho this book is forThis book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book. Gain practical skills in time series forecasting, time series analysis, and predictive modeling with Python. Learn ARIMA, SARIMA, gradient boosting, recurrent neural networks, and feature engineering through real-world forecasting examples. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…