Anomalies Detection in the Global Innovation Index’s Indicators
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Abstract
The measurement of a country's innovation capacity is essential for studies of trends and the identification of bottlenecks in a National InnovationSystem (NIs). In this context, the indicators utilized by the Global Innovation Index (GII) are crucial, since they support various researches and strategic decisions by investors, entrepreneurs and public agents. However, GII indicators are impacted by methodological changes and suffer from several types of practical problems such as measurement errors or missing data, generating anomalies in analyzes. Based on the premise of innovation incrementalism, the concept of anomaly was defined and a method was developed to automatically detect them, while classifying those resulting from methodological changes in opposition to those resulted from practical problems. The proposed method was applied to the indicators from the innovation outputs of Brazil, from 2013 to 2019, released by the GII.
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