Machine Learning Time Series Python by Ben Auffarth (31 results)

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Paperback or Softback. Condition: New. Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods. Book.

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Paperback. Condition: New. The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview.

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Paperback. Condition: New. The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview.

Paperback. Condition: 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.…

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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Paperback. Condition: 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.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Paperback. Condition: 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.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview.&Uumlber den Autor.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features:Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description:Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.What You Will Learn:Understand the main classes of time-series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning modelsBecome familiar with many libraries like prophet, xgboost, and TensorFlowWho this book is for:This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.…