Forecasting with time series imaging

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset

Adaptive Tag Selection for Image Annotation

Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-$k$ strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher F-score of 0.223. Moreover, by treating the underlying image annotation system as a black box, the new method can be used as an easy plug-in to boost the performance of existing systems

User Engagement with Mobile Technologies: A Multi-Dimensional Conceptualization of Technology Use

Our study conceptualizes user engagement – a form of technology use targeting the emerging ubiquitous mobile technology generation such as mobile health (mHealth) and social network applications. User engagement manifests in three dimensions, including behavioral, cognitive, and emotional engagement. We validated the measures (in both objective and subjective forms) for the three-dimension user engagement in two different mobile technology contexts, i.e., an e-nursing mobile application and a question-and-answer social network application. We further delineated the relationships among the three dimensions: 1) prior behavioral engagement contributed to both emotional and cognitive engagement, 2) emotional engagement lead to post behavioral engagement, and 3) emotional engagement, compared with prior behavioral engagement and cognitive engagement, exerted a stronger influence predicting post behavioral engagement. Our study enriches both technology use and engagement literature

Transformational Leadership and Digital Creativity: The Mediating Roles of Creative Self-Efficacy and Ambidextrous Learning

Drawing insights from social cognitive theory and organizational learning theory, this study aims to uncover themediating mechanisms between direct manager’s transformational leadership behaviors and employees’ digital creativity in the context of digital technology. We conducted a field survey in China and collected data from 234 employees who utilized digital technologies to support daily work. Structural equation modelling analysis results showed that employees’ creative self-efficacy and two learning activities (exploitation vs. exploration) effectively transmitted the influence of transformational leadership ondigital creativity. Our study not only contributes to the understanding on effective use of digital technologies, but also provides practical insights for managers in the big data era