Technology forecasting in the field of Apnea from online publications: Time series analysis on latent semantic

Analysis of technological trends and developments has been attempted in a number of previous publications using a quantitative method to measure the growth of science. Previous studies on this subject, however, put more emphasis on the frequency either to categorize the technological classes or to find the most prominent technology for a given period of time but less analysis on the future trends. This paper presents the time series analysis of the technological trends while employing the Latent Semantic Analysis to associate each technological term. In current study, we are interested on analyzing the correlation among different trends of terms in the area of biomedical technology to deal with Apnea sleep disorder (difficulty in breathing during sleep). We assume that the study will also applicable to the other areas of research. The technological terms within the concept having the highest trend identified in our experiment are the movement disorders, cystic fibrosis, dental prosthesis, microvascular angina, and esophageal sphincter. Performance evaluation during the experiment indicates that the Support Vector Regression outperform the other techniques, while statistical techniques such as Holt's and Winter's yield above average performance and comparable to the Polynomial method.