{"product_id":"machine-learning-for-time-series-with-python-9781837639397","title":"Machine Learning for Time Series with Python","description":"\u003cp\u003e\u003cb\u003eGet better insights from time-series data and become proficient in building models with real-world data \u003c\/b\u003e\u003c\/p\u003e\u003ch4\u003eKey Features\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eExplore time series forecasting and time series analysis in Python using ARIMA, SARIMA, GARCH, gradient boosting, and recurrent neural networks. \u003c\/li\u003e\n\u003cli\u003eImprove predictive modeling with feature engineering and forecasting machine learning techniques. \u003c\/li\u003e\n\u003cli\u003eApply demand forecasting and financial forecasting methods through practical case studies and real-world datasets.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eBook Description\u003c\/h4\u003eMachine 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, you’ll learn how to characterize datasets, measure and validate prediction performance, engineer features, and build forecasting pipelines.\nThe 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. We’ll 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.\nThrough practical examples, you’ll learn how to evaluate models, detect and handle anomalies and drift, and choose approaches that match data and operational needs.\u003ch4\u003eWhat you will learn\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eApply classical time series models such as ETS, ARIMA, and Theta\u003c\/li\u003e\n\u003cli\u003eValidate with SMAPE, MASE, RMSSE and rolling window, and walk-forward\u003c\/li\u003e\n\u003cli\u003eForecast as regression with sktime, darts, mlforecast, and skforecast\u003c\/li\u003e\n\u003cli\u003eUse modern deep learning like LSTMs, DLinear, TSMixer, and TimesNet\u003c\/li\u003e\n\u003cli\u003eGrasp foundation models such as TimesFM, OpenTSLM, or Chain-of-Charts\u003c\/li\u003e\n\u003cli\u003eQuantify uncertainty quantification with conformalized regression\u003c\/li\u003e\n\u003cli\u003eSet up feature stores, MLflow, and monitoring and scaling strategies\u003c\/li\u003e\n\u003cli\u003eHandle classification, drift, spatiotemporal and irregular time series\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eWho this book is for\u003c\/h4\u003eThis 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.","brand":"ThinkEDU","offers":[{"title":"Default Title","offer_id":60814862975262,"sku":"9781837639397","price":39.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_a02f881a-ea37-491a-9290-b78e54ae58e3.jpg?v=1793745475","url":"https:\/\/thinkedu.com\/products\/machine-learning-for-time-series-with-python-9781837639397","provider":"ThinkEDU","version":"1.0","type":"link"}