On The Statistical Analysis Of Series Of Observations
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On the Statistical Analysis of Series of Observations
It is no longer disputed nowadays that mathematical statistics is the main tool to be used in climatology. Mathematical statistics is in fact the science of random models and the purpose of the statistics analysis of series of observations is to from among the models which science makes available, the one which bes represents the behaviour of the observed phenomenon. Thi atitude may be justifed in two ways. From the theoretical point of view, the representation of the observad phenomenon by a model depending on aminimum number of paramenters anables objective statistical conslusions to be compared with the physical theories put forward, iether to confirm their validity or to reveal weaknesse: thus statistical analysis appears as a means for progress in the ofresearch. From the practical point view, due to the efficiency of methods of statistical analysis, their utilization ensures that the best use is made of the information accumulated dy the series of observations and that the maximum benefic will be derived from the capital represented by this information. If we think of the investments and operational cost which have been approved for the acquisition of the series of observations and of the economic value of these, which is continually demonstrated in the applications of meteorology and climatology, the necessity of such a position becomes obvious.
Introduction to Time Series Analysis
"Introduction to Time Series Analysis" is a comprehensive guide exploring the world of time series data, blending theoretical insights with practical applications. Time series analysis is crucial across disciplines like economics, finance, engineering, and environmental science, helping us understand past patterns, forecast future trends, and make informed decisions. We cater to students, researchers, and practitioners seeking a deep understanding of time series analysis. Covering a range of topics from foundational concepts to advanced techniques, we ensure readers gain a holistic view of the subject. With clear explanations, illustrative examples, and real-world case studies, this book equips readers with the knowledge and skills needed to tackle complex time series data effectively. The book provides a solid theoretical foundation in time series analysis, covering topics such as time series decomposition, forecasting methods, and advanced modeling techniques. Emphasis is placed on practical applications, with real-world examples and case studies illustrating concepts and methodologies. The text is written in clear and accessible language, suitable for readers with varying expertise, and acknowledges the interdisciplinary nature of time series analysis, exploring its applications across different fields. Whether you're a student, researcher, or practitioner, "Introduction to Time Series Analysis" offers valuable insights and practical guidance to harness the power of time series data for informed decision-making.
Basic Data Analysis for Time Series with R
Author: DeWayne R. Derryberry
language: en
Publisher: John Wiley & Sons
Release Date: 2014-06-23
Presents modern methods to analyzing data with multiple applications in a variety of scientific fields Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals. Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, Basic Data Analysis for Time Series with R also features: Real-world examples to provide readers with practical hands-on experience Multiple R software subroutines employed with graphical displays Numerous exercise sets intended to support readers understanding of the core concepts Specific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets