Wydawca: 8

Time Series Analysis with Python Cookbook. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Tarek A. Atwan

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch.Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.

Time Series Analysis with Python Cookbook. Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection - Second Edition

Tarek A. Atwan

To use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.

Time Series Analysis with Spark. A practical guide to processing, modeling, and forecasting time series with Apache Spark

Yoni Ramaswami, Dael Williamson, Jan Govaere

Written by Databricks Senior Solutions Architect Yoni Ramaswami, whose expertise in Data and AI has shaped innovative digital transformations across industries, this comprehensive guide bridges foundational concepts of time series analysis with the Spark framework and Databricks, preparing you to tackle real-world challenges with confidence.From preparing and processing large-scale time series datasets to building reliable models, this book offers practical techniques that scale effortlessly for big data environments. You’ll explore advanced topics such as scaling your analyses, deploying time series models into production, Generative AI, and leveraging Spark's latest features for cutting-edge applications across industries. Packed with hands-on examples and industry-relevant use cases, this guide is perfect for data engineers, ML engineers, data scientists, and analysts looking to enhance their expertise in handling large-scale time series data.By the end of this book, you’ll have mastered the skills to design and deploy robust, scalable time series models tailored to your unique project needs—qualifying you to excel in the rapidly evolving world of big data analytics.*Email sign-up and proof of purchase required

Time Series Indexing. Implement iSAX in Python to index time series with confidence

Mihalis Tsoukalos

Time series are everywhere, ranging from financial data and system metrics to weather stations and medical records. Being able to access, search, and compare time series data quickly is essential, and this comprehensive guide enables you to do just that by helping you explore SAX representation and the most effective time series index, iSAX.The book begins by teaching you about the implementation of SAX representation in Python as well as the iSAX index, along with the required theory sourced from academic research papers. The chapters are filled with figures and plots to help you follow the presented topics and understand key concepts easily. But what makes this book really great is that it contains the right amount of knowledge about time series indexing using the right amount of theory and practice so that you can work with time series and develop time series indexes successfully. Additionally, the presented code can be easily ported to any other modern programming language, such as Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript.By the end of this book, you'll have learned how to harness the power of iSAX and SAX representation to efficiently index and analyze time series data and will be equipped to develop your own time series indexes and effectively work with time series data.

Time Series with PyTorch. Modern Deep Learning Toolkit for Real-World Forecasting Challenges

Graeme Davidson, Lei Ma

Neural networks are powerful tools for time-series forecasting, but applying them effectively requires both practical experience and a clear understanding of architectures, training strategies, and evaluation methods. This book brings these ideas together in a structured and practical way.Starting with PyTorch fundamentals, you will build neural networks from scratch and progress through recurrent networks, attention mechanisms, and transformers before exploring forecasting architectures such as N-BEATS, N-HiTS, and the Temporal Fusion Transformer. Along the way, you will learn robust hyperparameter tuning, conformal prediction for uncertainty estimation, and reliable evaluation practices.Unlike most forecasting books, this text also explores topics often overlooked or treated separately, including transfer learning across collections of series, synthetic data generation with diffusion models, and self-supervised representation learning. Beyond forecasting, later chapters cover classification, clustering, anomaly detection, and embeddings for large-scale time-series modeling.Throughout, the focus is pragmatic: theory is reinforced through experimentation and implementation so you can apply these methods confidently to real-world time-series problems.

Time to race

Weronika Waszkiewicz

Musimy być szybsi. Szybsi, lepsi, sprytniejsi niż ci, którzy nas gonią Merci Lotte traci pamięć w wypadku samochodowym i na kilka miesięcy przenosi się do Miami. Mimo, iż mieszka w nim wraz z wujkiem i bratem, czuje się samotna w obcym mieście. Średnią szkołę decyduje się ukończyć w San Diego. Tu ma przyjaciół, tu jest u siebie. Tu spotyka chłopaka, który stoi za wszystkimi problemami, w jakie zostaje wplątana. Nielegalne wyścigi, nielegalne walki - oto realia, z jakimi wbrew własnej woli będzie musiała zmierzyć się Merci. Czy Logan Hill odważy się wyznać jej prawdę? Czy tych dwoje poradzi sobie z przeciwnościami, jakie na ich drodze stawia los? Czy rodzące się między nimi uczucie przetrwa próbę czasu i... zawrotnej szybkości? Time to race to pierwsza część trylogii Escape

Time to race

Weronika Waszkiewicz

Musimy być szybsi. Szybsi, lepsi, sprytniejsi niż ci, którzy nas gonią Merci Lotte traci pamięć w wypadku samochodowym i na kilka miesięcy przenosi się do Miami. Mimo, iż mieszka w nim wraz z wujkiem i bratem, czuje się samotna w obcym mieście. Średnią szkołę decyduje się ukończyć w San Diego. Tu ma przyjaciół, tu jest u siebie. Tu spotyka chłopaka, który stoi za wszystkimi problemami, w jakie zostaje wplątana. Nielegalne wyścigi, nielegalne walki - oto realia, z jakimi wbrew własnej woli będzie musiała zmierzyć się Merci. Czy Logan Hill odważy się wyznać jej prawdę? Czy tych dwoje poradzi sobie z przeciwnościami, jakie na ich drodze stawia los? Czy rodzące się między nimi uczucie przetrwa próbę czasu i... zawrotnej szybkości? Time to race to pierwsza część trylogii Escape

times new roman

Szczepan Kopyt

Szczepan Kopyt sale sale sale times new roman pamiętasz to elektryczne miasto jedliśmy w nim trupy zwierząt mieliśmy wyrafinowane rozrywki ach wszędzie autobusy uczelnie szanowne koła uprawialiśmy polityczną ekonomię tak jak się kiedyś grało kwartety sączyliśmy i sączyliśmy rytm mowy mieszał się z sosem sambal nasze zęby były poddawane zabiegom woziły nas samochody biegliśmy do sklepu po winogrona dla leżących w łóżku kobiet mieliśmy jądra albo cycki nie baliśmy si... Szczepan Kopyt ur. 1983 Najważniejsze dzieła:Yass 2005, Sale sale sale 2009, Buch 2011, Kir 2013 Poeta i muzyk, absolwent filozofii. Laureat Ogólnopolskiego Konkursu Poetyckiego im. Jacka Bierezina (2004). Nominowany do Paszportu Polityki (2011) oraz dwukrotnie do Nagrody Literackiej Gdynia (w 2010 i 2014). Jeden z głównych autorów nurtu poezji zaangażowanej. Nagrał dwa albumy z zespołem Yazzbot Mazzut, kolejne dwa w duecie z perkusistą Piotrem Kowalskim. Najbardziej znana piosenka tego duetu to wykonanie wiersza Kto zabił Jolantę Brzeską. Kupując książkę wspierasz fundację Nowoczesna Polska, która propaguje ideę wolnej kultury. Wolne Lektury to biblioteka internetowa, rozwijana pod patronatem Ministerstwa Edukacji Narodowej. W jej zbiorach znajduje się kilka tysięcy utworów, w tym wiele lektur szkolnych zalecanych do użytku przez MEN, które trafiły już do domeny publicznej. Wszystkie dzieła są odpowiednio opracowane - opatrzone przypisami oraz motywami.