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1 - Introduction to Time Series Analysis Lecture 1 Peter...

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Introduction to Time Series Analysis. Lecture 1. Peter Bartlett 1. Organizational issues. 2. Objectives of time series analysis. Examples. 3. Overview of the course. 4. Time series models. 5. Time series modelling: Chasing stationarity. 1
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Organizational Issues Peter Bartlett. [email protected] Office hours: Tue 11-12, Thu 10-11 (Evans 399). Joe Neeman. [email protected] Office hours: Wed 1:30–2:30, Fri 2-3 (Evans ???). http://www.stat.berkeley.edu/ bartlett/courses/153-fall2010/ Check it for announcements, assignments, slides, ... Text: Time Series Analysis and its Applications. With R Examples , Shumway and Stoffer. 2nd Edition. 2006. 2
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Organizational Issues Classroom and Computer Lab Section: Friday 9–11, in 344 Evans. Starting tomorrow, August 27: Sign up for computer accounts. Introduction to R. Assessment: Lab/Homework Assignments (25%): posted on the website. These involve a mix of pen-and-paper and computer exercises. You may use any programming language you choose (R, Splus, Matlab, python). Midterm Exams (30%): scheduled for October 7 and November 9, at the lecture. Project (10%): Analysis of a data set that you choose. Final Exam (35%): scheduled for Friday, December 17. 3
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A Time Series 0 1000 2000 3000 4000 5000 6000 7000 0 50 100 150 200 250 300 350 400 4
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A Time Series 1960 1965 1970 1975 1980 1985 1990 0 50 100 150 200 250 300 350 400 year 5
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A Time Series 1960 1965 1970 1975 1980 1985 1990 0 50 100 150 200 250 300 350 400 year $ 6
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A Time Series 1960 1965 1970 1975 1980 1985 1990 0 50 100 150 200 250 300 350 400 year $ SP500: 1960-1990 7
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A Time Series 1987 1987.05 1987.1 1987.15 1987.2 1987.25 1987.3 1987.35 1987.4 1987.45 1987.5 220 240 260 280 300 320 340 year $ SP500: Jan-Jun 1987 8
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A Time Series 240 250 260 270 280 290 300 310 0 5 10 15 20 25 30 $ SP500 Jan-Jun 1987. Histogram 9
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A Time Series 0 20 40 60 80 100 120 220 240 260 280 300 320 340 $ SP500: Jan-Jun 1987. Permuted. 10
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Objectives of Time Series Analysis 1. Compact description of data. 2. Interpretation. 3. Forecasting. 4. Control. 5. Hypothesis testing. 6. Simulation. 11
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Classical decomposition: An example Monthly sales for a souvenir shop at a beach resort town in Queensland.
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