Optimal Event Monitoring through Internet Mashup over Multivariate Time Series

Introduction

Observing behaviors, trends, and patterns on multivariate time series (Bisgaard & Kulahci, 2011; Chatfield, 2001) has been broadly used in various application domains, such as financial markets, medical treatments, economic studies, and electric power management. Domain experts utilize multiple time series to detect events and make better decisions. For example, financial analysts predict different states of the stock market, e.g., bull or bear, more accurately based upon monitoring daily stock prices, weekly interest rates, and monthly price indices. Physicians monitor patients’ health conditions by measuring their diastolic and systolic blood pressures, as well as their electrocardiogram tracings over time. Sociologists uncover hidden social problems within a community more profoundly through studying a variety of economic, medical, and social indicators, e.g., annual birth rates, mortality rates, accident rates, and various crime rates. The goal of examining those characteristics over multivariate time series on events is to support decision makers, e.g., financial analysts, physicians, and sociologists, to better understand a problem in different perspectives within a particular domain and to offer better actionable recommendations.

To support such an event-based decision-making and determination over multivariate time series, in this paper, we propose a Web-Mashup Application Service Framework for Multivariate Time Series Analytics (MTSA). This framework is an integrated tool to support the MTSA service development, including model definitions, querying, parameter learning, data monitoring, decision recommendations, and model evaluations. Domain experts could use the framework to develop and implement their web-based decision-making applications on the Internet. Using a Web Mashup function offered by the Web 2.0 technology (Vancea & Others, 2008; Gurram & Others, 2008; Murugesan, 2007;Bradley, 2008; Alonso & Others, 2004; Altinel & Others, 2007; Ennals & Others, 2007;Thor & Others, 2007) on our framework, domain experts could collect and unify global information and data from different channels and media, such as web sites, data sources, organizational information, etc., to generate a concentric view of collected time series data from which the learning service determines optimal decision parameters. Using optimal decision parameters, domain experts can employ the monitoring service to detect events and the recommendation service to suggest actions.

Presently, there are two key approaches that domain users utilize to identify and detect interesting events over multivariate time series. These approaches are domain-knowledge-based and formal-learning-based. The former approach completely relies on domain experts’ knowledge. Based on their knowledge and experience, domain experts determine monitoring conditions that detect events of interest and trigger an appropriate action. More specifically, domain experts, e.g., financial analysts, have identified several deterministic time series, such as the S&P 500 percentage decline (SPD) and the Consumer Confidence Index drop (CCD), from which they develop parametric monitoring templates, e.g., SPD < -20%, CCD < -30 (Stack, 2009), etc., according to their expertise. Once the incoming time series, i.e., SPD and CCD, satisfy the given templates at a particular time point, the financial analysts decide that the bear market bottom is coming, which is the best buy opportunity to purchase the stock to earn the maximal earning.