Time-Series Predictive Analysis of DAX 30

In this blog post we’ll examine some common techniques used in time-series analysis of DAX 30 by applying them to a data set containing daily closing values from 1990 up to present day.

The DAX (Deutscher Aktienindex (German stock index)) is a blue chip stock market index consisting of the 30 major German companies trading on the Frankfurt Stock Exchange. It is the equivalent of the FT 30 and the Dow Jones Industrial Average, and because of its small selection it does not necessarily represent the vitality of the economy as whole.

The objective is to explore some of the basic ideas and concepts from time series analysis, and observe their effects when applied to a real world data set. Although it’s not possible to actually predict changes in the index using these techniques, the ideas presented here could theoretically be used as part of a larger strategy involving many additional variables to conduct a regression or machine learning effort.

Time series analysis is a branch of statistics that involves reasoning about ordered sequences of related values in order to extract meaningful statistics and other characteristics of the data. It’s used in a wide range of disciplines including econometrics, signal processing, weather forecasting, and basically any other field that involves time series data. These techniques are often used to develop models that can be used to attempt to forecast future values of a series, either on their own or in concert with other variables.

Time-Series Predictive Analysis of DAX 30


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