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Charalampos Chanialidis,
Ludger Evers,
Jethro Browell,
Anna Sehn,
and
Marian Scott
Topics Covered
- Time series analysis
- Quantile regression and extremes
- Spatial Modelling
- Open Data and citizen science
- Forecasting and prediction
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Reading list
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Emphasizing practical applications, this book covers various time series analysis techniques and demonstrates their use through R examples. It provides a valuable resource for practitioners and students seeking to apply time series analysis in their own work.
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Established as a classic in the field, this book focuses on the fundamentals of time series analysis and forecasting, with an emphasis on understanding the underlying principles. It provides a solid foundation for students, researchers, and practitioners seeking a deeper understanding of time series analysis.
Offering a comprehensive overview of time series analysis, this book covers both theoretical foundations and practical applications. It explores various techniques for analyzing time series data, addressing topics such as stationarity, seasonality, forecasting, and more.
Provides a rigorous and in-depth treatment of the theory and methods used in time series analysis. It covers advanced topics such as stochastic processes, spectral analysis, state space models, and more. Suitable for advanced students and researchers.
This introductory-level book provides a clear and accessible overview of time series analysis. It covers basic concepts, techniques, and applications, making it suitable for beginners or those seeking a refresher in the subject.
Explores the application of machine learning techniques to time series forecasting. It covers various machine learning algorithms and provides practical guidance on implementing them for forecasting purposes.
Focuses on time series analysis using state space methods. It covers advanced topics such as Kalman filtering, smoothing, and forecasting, and is suitable for researchers and practitioners in fields where state space models are commonly used.
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Combines theoretical foundations and practical applications of time series analysis using the R programming language. It provides hands-on examples and exercises, making it suitable for students and practitioners seeking to implement time series analysis in R.
This introductory book provides a clear and concise overview of time series analysis and forecasting. It covers fundamental concepts, methods, and applications, making it accessible to students and practitioners with limited prior knowledge in the field.
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This comprehensive book covers a wide range of forecasting methods, from traditional statistical models to machine learning techniques. It provides a practical guide for practitioners and researchers seeking to implement effective forecasting solutions.
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Provides a comprehensive and rigorous treatment of time series analysis. It covers advanced topics such as non-stationary time series, cointegration, and multivariate time series models.
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This comprehensive textbook provides a broad overview of forecasting techniques, from basic to advanced methods. It is suitable for both undergraduate and graduate students, as well as practitioners in various fields.
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This classic textbook comprehensive reference on statistical methods for forecasting. It covers a wide range of topics, from time series analysis to Bayesian forecasting.
Provides a clear and concise introduction to time series analysis and forecasting. It is suitable for undergraduate and graduate students, as well as practitioners who need to analyze and forecast time series data.
Focuses on forecasting economic and financial time series. It covers a wide range of topics, from model selection to forecasting evaluation.
Provides a clear and concise introduction to time series analysis and forecasting. It is suitable for undergraduate and graduate students, as well as practitioners who need to analyze and forecast time series data.
Provides a comprehensive overview of machine learning and deep learning methods for time series forecasting. It is suitable for both undergraduate and graduate students, as well as practitioners who need to develop accurate and reliable forecasts.
Focuses on forecasting in supply chains. It covers a wide range of topics, from demand forecasting to inventory management.
Provides a clear and concise introduction to statistical forecasting. It is suitable for undergraduate and graduate students, as well as practitioners who need to develop accurate and reliable forecasts.
Provides a comprehensive overview of statistical methods for prediction, covering both traditional and modern approaches. It is an excellent resource for anyone who wants to learn more about the theory and practice of prediction.
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