Abstract
AbstractWe present an idea to group time series according to similarity of their local trends and to predict future direction of the trend of all of them on the basis of forecast of only one representative. First, we assign to each time series an adjoint one, which consists of a sequence of the F1-transform components. Then, they are grouped together according to their similarity, a principal time series is selected in each group and its future course is forecasted. Finally, directions of trends of the other time series from the same group are computed using special methods of fuzzy natural logic.
Highlights
In economic applications we usually deal with many interrelated time series
We suggest to use the technique of F-transform that was already proved to be very effective in the analysis of time series
We developed a new method for forecasting trends of a group of time series with similar course of their local trends
Summary
In economic applications we usually deal with many interrelated time series. This means that a dynamic behavior of one time series influences the dynamic behavior of the other one. Future values of one time series depend on its past values and on past values of the other time series. A given time series can be taken as a result of measurements (in time) of some endogenous variable and so, to obtain its forecast we must compute forecast of values of the corresponding exogenous variables on which the given variable is dependent. The situation can be quite complex because the interrelations among time series may lead to a hierarchical structure and values of the given time series can be obtained using a structured aggregation of the other ones
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