Research And Practice Of Time Series Data Mining And Visualization

The successful combination of data mining and visualization technology make visualization technology penetrate into every step of data mining. Specifically, users can see the whole process of data processing, the speed and the depth of system excavation can be greatly improved and the possibilities of extracting new knowledge can also be increased.Based on cross-industry standard process for Data Mining (CRISP-DM), this paper researched and implemented a time series forecasting system which is mining process visualized. The proposed system was applied to rail transit AFC operation and management's data analysis. It has been proved that our system has a very good commercial application value.The main research activities include the following aspects:1) Analyze the data mining, time series data mining, data mining visualization and other related technology, and studied mainly on the visualization technology and its implementation of data mining process.2) Designed and implemented the three-layer structure and its corresponding data mining, processing logic of the four-level hierarchy of the time series forecasting system whose mining process is visualized, to improve the system performance.3) Studied the radial basis function neural network (RBFNN), analyzed the principle and feasibility of RBFNN for time series prediction. Designed and implemented the time sequence forecasting system based on RBFNN model, and carried out by Matlab simulation experiment.4) According to CRISP-DM process model, shows the operation steps and results of the time series forecasting system. Users can participate in the whole process of mining. Including the control data flow and adjust mining steps, etc, so as to obtain highly reliable prediction results.The experimental results demonstrate that the design and practice of the forecasting system is correct and feasible. The interface of the forecasting system is flexible and friendly, which can provide credible prediction of the rail transportation business data and support the application of decision-making.