2. Incremental Statistical Measures

Statistical measures provide essential and valuable information about data and are needed for any kind of data analysis. Statistical measures can be used in a purely exploratory context to describe properties of the data, but also as estimators for model parameters or in the context of hypothesis testing. For example, the mean value is a measure for location, but also an estimator for the expected value of a probability distribution from which the data are sampled. Statistical moments of higher order than the mean provide information about the variance, the skewness, and the kurtosis of a probability distribution. The Pearson correlation coefficient is a measure for linear dependency between two variables. In robust statistics, quantiles play an important role, since they are less sensitive to outliers. The median is an alternative measure of location, the interquartile range an alternative measure of dispersion. The application of statistical measures to data streams requires online calculation. Since data come in step by step, incremental calculations are needed to avoid to start the computation process each time new data arrive and to save memory so that not the whole data set needs to be kept in the memory. Statistical measures like the mean, the variance, moments in general, and the Pearson correlation coefficient render themselves easily to incremental computations, whereas recursive or incremental algorithms for quantiles are not as simple or obvious. Nonstationarity is another important aspect of data streams that needs to be taken into account. This means that the parameters of the underlying sampling distribution might change over time. Change detection and online adaptation of statistical estimators is required for nonstationary data streams. Hypothesis tests like the χ²- or the t-test can be a basis for change detection, since they can also be calculated in an incremental fashion. Based on change detection strategies, one can derive information on the sampling strategy, for instance the optimal size of a time window for parameter estimations of nonstationary data streams.