Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion

With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.

Spectral Absorption Gas Sensor Based on Anti-Resonant Reflecting Optical Waveguide

空气硅石微观结构光纤维基于反反响的思考光波导(箭头) ，原则被用来发展一光谱吸收气体传感器。箭头纤维有一个空气核心和空气 cladding 层。有一段 725 公里的箭头纤维被用来构造一个察觉到的系统检测乙炔气体。气体从纤维的一结束被注入纤维。传播系列用一个光光谱分析器被收集。结果显示系统能检测不同集中的气体并且有好系统线性。系统的反应时间是大约 200 s。