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Chapter 1 Introduction A time series is a series of observations xt, observed over a period of time. Typically the observations can be over an entire interval, randomly sampled on an interval or at fixed time points. Different types of time sampling require different approaches to the data analysis. In this course we will...
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Timewhitenoise 0 50 100 150 200-2-1 0 1 2Figure 1. 1: Plot of independent uncorrelated random variables In Figure 1. 2 we give a plot of monthly SOI from January 1876-July 2014 (note that there is some doubt on the reliability of the data before 1930). The data was obtained from http://www. bom. gov. au/climate/current...
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Nasdaq Data from 1985-present The daily closing Nasdaq price from 1st October, 1985-8th August, 2014 is given in Figure 1. 3. The (historical) data was obtained from https://uk. finance. yahoo. com. See also http://www. federalreserve. gov/releases/h10/Hist/. Of course with this type of data the goal is to make money! ...
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Timesunspot1 1700 1750 1800 1850 1900 1950 20000 50 100 150Figure 1. 4: Plot of Sunspot numbers 1700-2013 the monthly temperatures from January 1996-July 2014. The data was obtained from http://data. giss. nasa. gov/gistemp/graphs_v3/Fig. A2. txt and http://data. giss. nasa. gov/gistemp/graphs_v3/Fig. C. txt respective...
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Timetemp 1880 1900 1920 1940 1960 1980 2000-0. 5 0. 0 0. 5Figure 1. 5: Plot of global, yearly average, temperature anomalies, 1880-2013 Timemonthlytemp1 2000 2005 2010 20150. 2 0. 4 0. 6 0. 8 Figure 1. 6: Plot of global, monthly average, temperatures January, 1996-July, 2014. # where 1 denotes January. Frequency = numb...
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1. 3 Filtering time series Often we transform data to highlight features or remove unwanted features. This is often done by taking the log transform or a linear transform. It is no different for time series. Often a transformed time series can be easier to analyse or contain features not apparent in the original time se...
TS.pdf
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