Systematic financial risk detection based on DTW dynamic algorithm and sensor network

Traditional financial risk detection methods are mainly based on statistical models and market data, in which sensor network has a wide application prospect. The research can improve the accuracy and efficiency of financial risk detection by using the data and information of sensor network. By making full use of the data collection capabilities of sensor networks to obtain more comprehensive and accurate financial data, it helps to identify potential risk factors more accurately. This paper introduces DTW (Dynamic Time warping) algorithm as the main financial risk detection method, which can effectively capture the similarity between time series data and apply it to the financial data obtained by sensor network. Through regularization and matching of time series data, abnormal changes and abnormal patterns can be identified, so as to timely warn and control financial risks. By comparing the data of sensor network with that of traditional methods, we found that the financial risk detection method based on DTW algorithm and sensor network has higher accuracy and efficiency, and can identify potential risk factors more accurately.