The quark and the jaguar: adventures in the simple and the complex
The quark and the jaguar: adventures in the simple and the complex
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Data mining for unemployment rate prediction using search engine query data
Service Oriented Computing and Applications
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This study concerns the fields of economics, labor relations, and the dynamics of complex systems. We consider a specific phenomenon in the dynamics of unemployment-episodes of a sharp increase in the unemployment rate, called here ''fast acceleration of unemployment'' (FAU). Our study is a ''technical'' analysis that is a heuristic search of phenomena preceding FAUs. We use the methodology of pattern recognition of infrequent events developed by the artificial intelligence school of Gelfand for a study of rare phenomena of highly complex origin, that, by their nature, limit the possibilities of using classical statistical or econometric methods. Our goal is to identify by an analysis of macroeconomic indicators a robust and rigidly defined prediction algorithm of the ''yes or no'' variety indicating at any time moment, whether a FAU should be expected or not within the subsequent months. Considering unemployment in France between 1962 and 1997, we have found a specific ''premonitory'' pattern of three macroeconomic indicators that may be used for algorithmic prediction of FAUs. Among seven FAUs identified within these years six are preceded within 12 months by this pattern that appears at no other time. The application of this algorithm to Germany, Italy and the USA yields similar results. Such predictability reflects the fact that the economy, like other complex systems, exhibits regular collective behavior patterns. The final test, as in any prediction research, should be advance prediction. The first such prediction, for the USA for early 2000, has been correct.