Time series analysis concerns the mathematical modeling of time varying phenomena, e.g., ocean waves, water levels in lakes and rivers, demand for electrical power, radar signals, muscular reactions, ECG-signals, or option prices at the stock market. The structure of the model is chosen both with regard to the physical knowledge of the process, as well as using observed data. Central problems are the properties of different models and their prediction ability, estimation of the model parameters, and the model's ability to accurately describe the data. Consideration must be given to both the need for fast calculations and to the presence of measurement errors. This book gives a comprehensive presentation of stochastic models and methods in time series analysis.

LINKS

• Matlab files
• Python files
• Related course
• Errata (4th edition, published 2021)
• Errata (3rd edition, published 2019)
• Errata (2nd edition, published 2015)
• Errata (1st edition, published 2013)

 For the older editions, please also refer the the errata of the more recent editions.