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