A Distributed Algorithm for Indexing Power Time Series - WU Yi; GUO Qi-lin; CHEN Hao-tian; GUO Nai-wang (Journal of Harbin University of Science and Technology, 2021)
Time series research has been applied to more and more areas. More and more domain applications need to index and analyze massive time series, such as finance, electricity, bioinformatics, and so on. Such applications are often faced with hundreds of millions of time series of processing, and then identify some hidden pattern from the model. Firstly, we give a simple idea of the distributed indexing algorithm based on Isax, which points out its existing problems. Then we propose a distributed indexing algorithm to establish the index structure and then insert the time series to the corresponding leaf node. Finally, this paper constructs a complete approximation query system of power time series, and greatly improves the computational efficiency under the premise of ensuring the accuracy of query. The correctness, efficiency and expansibility of the algorithm are proved on the experimental data set