RESUMO
This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.
RESUMO
An ever increasing amount of medical data such as electronic health records, is being collected, stored, shared and managed in large online health information systems and electronic medical record systems (EMR) (Williams et al., 2001; Virtanen, 2009; Huang and Liou, 2007) [1-3]. From such rich collections, data is often published in the form of census and statistical data sets for the purpose of knowledge sharing and enabling medical research. This brings with it an increasing need for protecting individual people privacy, and it becomes an issue of great importance especially when information about patients is exposed to the public. While the concept of data privacy has been comprehensively studied for relational data, models and algorithms addressing the distinct differences and complex structure of XML data are yet to be explored. Currently, the common compromise method is to convert private XML data into relational data for publication. This ad hoc approach results in significant loss of useful semantic information previously carried in the private XML data. Health data often has very complex structure, which is best expressed in XML. In fact, XML is the standard format for exchanging (e.g. HL7 version 3(1)) and publishing health information. Lack of means to deal directly with data in XML format is inevitably a serious drawback. In this paper we propose a novel privacy protection model for XML, and an algorithm for implementing this model. We provide general rules, both for transforming a private XML schema into a published XML schema, and for mapping private XML data to the new privacy-protected published XML data. In addition, we propose a new privacy property, δ-dependency, which can be applied to both relational and XML data, and that takes into consideration the hierarchical nature of sensitive data (as opposed to "quasi-identifiers"). Lastly, we provide an implementation of our model, algorithm and privacy property, and perform an experimental analysis, to demonstrate the proposed privacy scheme in practical application.
