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Background
Time series come in all shapes and sizes. They arise in many walks of life - almost anything you can think of can be associated with a time series. Notable examples are: (i) economic time series, e.g. stock prices, economic indices such as inflation or unemployment, sales figures; (ii) medical time series, e.g. various vital statistics such as ECG, EEG, MRI scans, spread of disease; (iii) environmental series such as temperature, water level, pollution level; (iv) network series: transportation networks, internet traffic, social networks, epidemics; (v) energy series: demand, supply... the list is endless.
Time series have unique characteristics that are not shared with `ordinary' data. First, time series data have an order, you can't jumble the observations up and obtain the same series. Second, one can forecast the future with time series methods and obtain some idea of the uncertainty of the forecast. Generally speaking, we are not good at forecasting the future! In some domains (e.g. energy) we are very good. However, typically in these areas small improvements can yield huge dividends.
Much classical research investigates an area called stationary time series (see box, right). However, these models are often not appropriate for many real time series where the environment or underlying processes change their form over time. My main research into time series is in locally stationary time series.
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