File Name: intro to time series and forecasting .zip
- Introduction to Time Series Forecasting
- Introduction to Time Series Analysis and Forecasting in R
- Introduction to Time Series Analysis and Forecasting
Introduction to Time Series Forecasting
This web site contains notes and materials for an advanced elective course on statistical forecasting that is taught at the Fuqua School of Business, Duke University. It covers linear regression and time series forecasting models as well as general principles of thoughtful data analysis. The time series material is illustrated with output produced by Statgraphics , a statistical software package that is highly interactive and has good features for testing and comparing models, including a parallel-model forecasting procedure that I designed many years ago. The material on multivariate data analysis and linear regression is illustrated with output produced by RegressIt , a free Excel add-in which I also designed. However, these notes are platform-independent. Any statistical software package ought to provide the analytical capabilities needed for the various topics covered here. If you use Excel in your work or in your teaching to any extent, you should check out the latest release of RegressIt, a free Excel add-in for linear and logistic regression.
Time series data is data that is collected at different points in time. This is opposed to cross-sectional data which observes individuals, companies, etc. Because data points in time series are collected at adjacent time periods there is potential for correlation between observations. This is one of the features that distinguishes time series data from cross-sectional data. Time series data can be found in economics , social sciences , finance , epidemiology , and the physical sciences. The statistical characteristics of time series data often violate the assumptions of conventional statistical methods. Because of this, analyzing time series data requires a unique set of tools and methods, collectively known as time series analysis.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Montgomery and C. Jennings and M. Montgomery , C.
Introduction to Time Series Analysis and Forecasting in R
It seems that you're in Germany. We have a dedicated site for Germany. Authors: Brockwell , Peter J. Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics.
I would have no hesitation in recommending it to my students. It allows one to integrate theoretical discourse and methodologic practice with considerable ease. Those who are teaching from other texts are unnecessarily complicating their lives. It provides an excellent introduction into time series analysis. Moreover, it is suitable as a reference book for practitioners. The great number of examples coming from economics, engineering, natural and social sciences contribute to a better understanding of the methods.
Introduction to Time Series Analysis and Forecasting
ARIMA models for time series forecasting. A random variable that is a time series is stationary if its statistical properties are all constant over time. A stationary series has no trend, its variations around its mean have a constant amplitude, and it wiggles in a consistent fashion , i.
Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time series models to attain adequate statistical power. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user to properly apply these techniques. Upper level undergraduate and graduate students, professors, and researchers studying: time series analysis and forecasting; longitudinal quantitative analysis; and quantitative policy analysis.
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