Modern Time Series with R. Practical forecasting and causal inference with tidy, reproducible workflows Yeasmin Khandakar, Roman Ahmed

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Autorzy:: Yeasmin Khandakar, Roman Ahmed
Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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129,00 zł

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Modern Time Series Analysis with R provides a comprehensive, hands-on guide to mastering the art of time series analysis using the R programming language. Written by leading experts in applied statistics and econometrics, this book helps data scientists, analysts, and developers bridge the gap between traditional statistical theory and practical business applications.
Starting with the foundations of R and tidyverse, you’ll explore the core components of time series data, data wrangling, and visualization techniques. The book then guides you through key modeling approaches—ranging from classical methods like ARIMA and Exponential Smoothing to advanced computational techniques such as machine learning, deep learning, and ensemble forecasting.
Beyond forecasting, you’ll discover how time series can be applied for causal inference, anomaly detection, change point analysis, and multiple time series modeling. Practical examples and reproducible code will empower you to assess business problems, choose optimal solutions, and communicate results effectively through dynamic R-based reporting.
By the end of this book, you’ll be confident in applying modern time series methods to real-world data, delivering actionable insights for strategic decision-making in business, finance, technology, and beyond.

O autorach książki

Yeasmin Khandakar, PhD is a data scientist and statistician with 10+ years of experience building time series and machine learning solutions across industry and academia. She developed an automatic ARIMA modeling framework during her doctoral research under Prof. Rob Hyndman and has published highly cited work on automatic ARIMA forecasting. In industry, Yeasmin has led analytics and forecasting initiatives as Senior Data Scientist at Transurban, Officeworks, Coles, and Optalert, and previously designed automated trading algorithms as Senior Research Scientist at Portland House Group. Her expertise spans time series filtering and forecasting, predictive modeling, clustering, and large-scale model development in R and Python. Yeasmin holds a PhD in Statistics from Monash University, is NV1 cleared, and is certified as a Professional Scrum Product Owner (PSPO I). She brings deep practical and research insight to Modern Time Series Analysis with R

Roman Ahmed, PhD is a data science and analytics leader with 15+ years across industry and academia, specializing in statistics, econometrics, forecasting, A B testing, and ML AI. He currently serves as an Analytics Manager (Senior Specialist) at Optus and has held senior roles at Jobs and Skills Australia, Xero, the Australian Taxation Office, ANZ, Telstra, and Experian. Roman has led teams to build experimentation platforms, benefit-measurement frameworks, and large-scale time-series and econometric models that inform executive decision-making. He works fluently across R, Python, SQL, and STATA, deploys solutions in cloud environments such as Microsoft Azure, and has applied NLP and clustering to unlock insights from unstructured data. Roman earned his PhD in Econometrics and Business Statistics from Monash University and a BSc in Applied Statistics from the University of Dhaka. He is NV1 cleared and bilingual (English, Bengali). He brings a rigorous, business-first perspective to Modern Time Series Analysis with R.

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Szczegóły książki

Tytuł oryginału:: Modern Time Series with R. Practical forecasting and causal inference with tidy, reproducible workflows.
ISBN Ebooka:: 978-18-051-2430-6, 9781805124306
Data wydania ebooka :: 2026-02-20 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski

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Kategorie:
Aplikacje biurowe » Inne
Programowanie » Algorytmy - Programowanie

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Produkt nie został jeszcze oceniony pod kątem ułatwień dostępu lub nie podano żadnych informacji o ułatwieniach dostępu lub są one niewystarczające. Prawdopodobnie Wydawca/Dostawca jeszcze nie umożliwił dokonania walidacji produktu lub nie przekazał odpowiednich informacji na temat jego dostępności.

Spis treści książki

1. R, RStudio and R packages
2. Writing functions in R
3. Loading data into R workspace
4. Time series Characteristics
5. Time Series Data Wrangling
6. Time Series Visualisation
7. Time Series Problem Spaces
8. Time Series Decomposition
9. Time Series Smoothing
10. Seasonality Analysis
11. Time Series Features
12. Forecasting models for univariate time series
13. Bayesian forecasting models
14. Machine Learning Forecasting Methods
15. Deep Learning forecasting Models
16. Model Evaluation and Measure Forecast Accuracy
17. Anomaly Detection and Imputation

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Modern Time Series with R. Practical forecasting and causal inference with tidy, reproducible workflows Yeasmin Khandakar, Roman Ahmed

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