PPT-Time-series data analysis

Why deal with sequential data Because all data is sequential All data items arrive in the data store in some order Examples transaction data documents and words In some or many cases the order does not matter. Flowserve Worcester Controls Series 44 threepiece ball valves for many years the most respected ball valve design in the industry are now better than ever A major research design and testing program brings you a new valve designed to ANSI B1634 spec Basic time series. Data on the outcome of a variable or variables in different time periods are known as time-series data.. Time-series data are prevalent in finance and can be particularly challenging because. Lectures: Each . Tuesday at . 16:00. . (First lecture: . May 21, . last lecture: . June 25. ). Thomas . Kreuz. , ISC, . CNR. . thomas.kreuz@cnr.it. . http://www.fi.isc.cnr.it/users/thomas.kreuz. Authors. Jessica Lin. Eamonn. Keogh. Li Wei. Stefano . Lonardi. Presenter. Arif. Bin . Hossain. Slides incorporate materials kindly provided by Prof. . Eamonn. Keogh. Time Series.  A . time series. Introduction to Time Series Analysis. A . time-series. is a set of observations on a quantitative variable collected over time.. Examples. Dow Jones Industrial Averages. Historical data on sales, inventory, customer counts, interest rates, costs, etc. FOGMEx. (. tsfit. /. tsview. ), CATS and Hector. M. Floyd K. Palamartchouk. Massachusetts Institute of Technology Newcastle University. GAMIT-GLOBK course. Africa Regional Workshop on the Building of Sustainable National. Greenhouse Gas Inventory Management Systems, and the Use of the 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Swakopmund. 1. 2. : . autocovariance. function of the individual time series . 3. Vector ARMA models. if the roots of the equation. are all greater than 1 in absolute value . Then : infinite MA representation.  . Eamonn Keogh . With. Yan Zhu, Chin-. Chia. Michael . Yeh. , Abdullah Mueen. . with contributions from Zachary Zimmerman, Nader . Shakibay. . Senobari. ,, Gareth Funning, Philip Brisk, Liudmila Ulanova, Nurjahan Begum, . Danny Hendler . hendlerd@post.bgu.ac.il. Amir Rubin . amirrub@post.bgu.ac.il. Agenda. Introduction to time series analysis. Euclidean distance. Dynamic time wrapping. Mini project TSA. Agenda. Introduction to time series analysis. Jean . Shimer. . and Patti . Fougere. , MA Part C. Karen Walker, WA Part . C. Karie. Taylor, AZ Part C. Abby . Winer, . DaSy. , ECTA. Tony Ruggiero, . DaSy. , . IDC. 2014 Improving Data, Improving Outcomes Conference. Chapter 18. Learning Objectives. LO18-1. Define and describe the components of a time series.. LO18-2. Smooth a time series by computing a moving average.. LO18-3. Smooth a time series by computing a weighted moving average.. STAT 689. forecasting. Forecasting is the process of making predictions of the future based on past and present data!. forecasting. Coming up with predictions is important.. It is also very hard since none has the correct model of the world.. Dr . Milena . Čukić. Dpt. General Physiology with Biophysics. University of Belgrade, Serbia. Complex dynamics of living systems. Living organisms are complex both in their structures and functions. Parameters of human physiological functions such as arterial blood pressure (.