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<!DOCTYPE HTML>
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<title>Change Detection in Nonstationary Time Series</title>
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<h1><font face="Euclid" size="6">Statistical Machine Learning Approach for Change Detection in Nonstationary Time Series</font>
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<p>Real-world time series are non-stationary, means that their statistical properties change with time. In this case, the goal of modeling is to find pattern of change in time series.
Main contribution of my PhD thesis is developing a new method based on regression, heuristic optimization and Bayesian information theory that find the model of some “regimes” non-stationary time series.
<p><span class="image main"><img src="images/CD1.jpg" alt="" style="width: 50%; height: 50%" />
<span>Anomaly of temperature over United States during 1915-2014. This is a typical sample of non-stationary time series that a simple linear regression is not adequate for modeling.
</span>
<p>My analysis shows that the time series of the US temperature should be modeled with two linear trends with a change point in 1958.
Here, the top figure shows the linear trends in 1900-1958 for 1189 stations, and the bottom figure demonstrates the 1958-2014 trends. Our country is warming, especially in northeast and southwest.
What happened in 1958? The industrialization after WW2 increased the amount of greenhouse gases.<p>
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