Prediction of events based on Complex Event Processing and Probabilistic Fuzzy Logic

Abstract

This paper proposes a prediction system named as PECEP. PECEP is based on Complex Event Processing (CEP) and Probabilistic Fuzzy Logic (PFL). CEP is event processing that combines data from multiple sources to infer events or patterns that suggest more complicated circumstances. The main aim of CEP is to identify meaningful events such as opportunities or threats and respond to them as quickly as possible. A PFL is a type of logic that recognizes more than simple truth values. Fuzzy logic can be represented with degrees of truthfulness and falsehood. The event data are downloaded and updated dynamically from the online data source. PECEP consists of three steps namely Collection of Data Set, Feature Processing and Machine Learning. PECEP is validated using Esper in the stock market domain. A stock market or equity market is a public entity for the trading of company stock and derivatives at an agreed price. The output of the PECEP provides a guideline about the future price of stock. The performance of PECEP is compared with the existing system in terms of the four parameters- Accuracy, Error Rate Analysis, Processing Time and Throughput.