Sari direct la continut
Peste 10 milioane de produse, intr-un singur loc.
Produs

Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation, Paperback/Tarek A. Atwan

Vânzător
elefant.ro elefant.ro
Pret
477.99 Lei
Categorie (vânzător)
Foreign Books
Marca
Packt Publishing

Produs actualizat în urmă cu 11 ore
Descriere YEO:

Time Series Analysis with Python - Disponibil la elefant.ro

Pe YEO găsești Time Series Analysis with Python de la Packt Publishing, în categoria Foreign Books.

Indiferent de nevoile tale, Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation, Paperback/Tarek A. Atwan din categoria Foreign Books îți poate aduce un echilibru perfect între calitate și preț, cu avantaje practice și moderne.

Preț: 477.99 Lei

Caracteristicile produsului Time Series Analysis with Python

  • Brand: Packt Publishing
  • Categoria: Foreign Books
  • Magazin: elefant.ro
  • Ultima actualizare: 30-05-2026 00:29:25

Comandă Time Series Analysis with Python Online, Simplu și Rapid

Prin intermediul platformei YEO, poți comanda Time Series Analysis with Python de la elefant.ro rapid și în siguranță. Bucură-te de o experiență de cumpărături online optimizată și descoperă cele mai bune oferte actualizate constant.


Descriere magazin:
Perform time series analysis and forecasting confidently with this Python code bank and reference manual Key Features: Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms Learn different techniques for evaluating, diagnosing, and optimizing your models Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities Book Description: 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. What You Will Learn: Understand what makes time series data different from other data Apply various imputation and interpolation strategies for missing data Implement different models for univariate and multivariate time series Use different deep learning libraries such as TensorFlow, Keras, and PyTorch Plot interactive time series visualizations using hvPlot Explore state-space models and the unobserved components model (UCM) Detect anomalies using statistical and machine learning methods Forecast complex time series with multiple seasonal patterns Who this book is for: This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book. Book specifications: Dimensions: 235 x 190 Author: Tarek A. Atwan Cover type: Paperback Publishing Year: 2022 Publishing Month: 6 Pages: 630 Language: English Publisher: Packt Publishing Weight: 1066 g

Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation, Paperback/Tarek A. Atwan - 0 | YEO

Produse asemănătoare

Produse marca Packt Publishing