Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given m source streams (X1,X2,...,Xm) and one target data stream Y , at any time window w,  want to find out which source stream has the strongest dependency to the current target stream value. Existing solutions fail in several important dependency cases, such as the not-similar-but-frequent pat- terns, the signals with multiple lags, and the single point dependencies. To reveal these hard-to-detect local patterns in streams, a statistical model based framework is developed, together with an incremental update algorithm. Using the frame- work, a new scoring function based on the conditional probability is defined to effectively capture the local dependencies beten any source stream and the tar- get stream. Immediate real life applications include quickly identifying the causal streams with respect to a Key Performance Indicator (KPI) in a complex produc- tion system, and detecting locally correlated stocks for an interesting event in the financial system.  apply this framework to two real data sets to demonstrate its advantages compared with the Principal Component Analysis (PCA) based method (16) and the naive local Pearson implementation.
['Qiyang Duan', 'Mingxi Wu', 'Peng Wang', 'Wei Wang', 'Yu Cao']
I am writing a research paper on a idea A Probabilistic Approach to Detect Local Dependencies in Streams
[{"from": "human", "value": "I am writing a research paper on a idea A Probabilistic Approach to Detect Local Dependencies in Streams"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given m source streams (X1,X2,...,Xm) and one target data stream Y , at any time window w,  want to find out which source stream has the strongest dependency to the current target stream value. Existing solutions fail in several important dependency cases, such as the not-similar-but-frequent pat- terns, the signals with multiple lags, and the single point dependencies. To reveal these hard-to-detect local patterns in streams, a statistical model based framework is developed, together with an incremental update algorithm. Using the frame- work, a new scoring function based on the conditional probability is defined to effectively capture the local dependencies beten any source stream and the tar- get stream. Immediate real life applications include quickly identifying the causal streams with respect to a Key Performance Indicator (KPI) in a complex produc- tion system, and detecting locally correlated stocks for an interesting event in the financial system.  apply this framework to two real data sets to demonstrate its advantages compared with the Principal Component Analysis (PCA) based method (16) and the naive local Pearson implementation."}]