Name: Time Series Analysis and Forecasting using Python & R (en Inglés)
Price: 315.14 PEN
Availability: InStock
Author: Strickland, Jeffrey
ISBN: 9781716451133

portada Time Series Analysis and Forecasting using Python & R (en Inglés)

Formato

Libro Físico

Autor

Jeffrey Strickland

Editorial

Lulu.com

Idioma

Inglés

N° páginas

448

Encuadernación

Tapa Dura

Dimensiones

22.9 x 15.2 x 2.5 cm

Peso

0.77 kg.

ISBN13

9781716451133

Categorías

Lenguajes de programación scripting

Time Series Analysis and Forecasting using Python & R (en Inglés)

Jeffrey Strickland (Autor) · Lulu.com · Tapa Dura

Libro Físico

S/ 315,14

S/ 630,28

Ahorras: S/ 315,14

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Viernes 28 de Junio y el Viernes 12 de Julio.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Time Series Analysis and Forecasting using Python & R (en Inglés)"

This book full-color textbook assumes a basic understanding of statistics and mathematical or statistical modeling. Although a little programming experience would be nice, but it is not required. We use current real-world data, like COVID-19, to motivate times series analysis have three thread problems that appear in nearly every chapter: "Got Milk?", "Got a Job?" and "Where's the Beef?" Chapter 1: Loading data in the R-Studio and Jupyter Notebook environments. Chapter 2: Components of a times series and decomposition Chapter 3: Moving averages (MAs) and COVID-19 Chapter 4: Simple exponential smoothing (SES), Holt's and Holt-Winter's double and triple exponential smoothing Chapter 5: Python programming in Jupyter Notebook for the concepts covered in Chapters 2, 3 and 4 Chapter 6: Stationarity and differencing, including unit root tests. Chapter 7: ARIMA and SARMIA (seasonal) modeling and forecast development Chapter 8: ARIMA modeling using Python Chapter 9: Structural models and analysis using unobserved component models (UCMs) Chapter 10: Advanced time series analysis, including time-series interventions, exogenous regressors, and vector autoregressive (VAR) processes.