Time-series Analysis on Singapore Public Transportation Train Network Methodology
| Background | Data Source | Methodology |
|---|
| Tools used |
For data preparation, descriptive statistics, we use SAS JMP Pro and SAS Enterprise Guide. We used both tools as we are familiar with SAS Enterprise Guide as the Analytics Foundation course uses SAS Enterprise Guide; therefore we are well versed in the tool. We use SAS JMP Pro as recommended by our project supervisor as a faster alternative. However, as we use both tools interchangeable as fit the task.
For the data-mining portion, we will use SAS Enterprise Miner as the tool for time series data mining. SAS Enterprise Miner, is an analytical software that streamlines and simplifies data mining process which allows user to perform descriptive, predictive and time-series analysis on huge volumes of data. The software has interactive visualization functions and its user interface allows easy interaction by drag and drop functionality.
| Methodolgy |
| Time-series Data Mining - Dynamic Time Warping Algorithm |
DTW algorithm is often used to overcome this problem of varying lengths. The DTW algorithm is computed to identify two time-series sequence based on Euclidean Distance (ED), which aligns the time-series by creating warping matrix to search for optimal path. The elastic shifting in time-domain matches sequence that are similar but out of phase.
| Time-series Data Mining - SAS Enterprise Miner Process |
The diagram below shows the SAS Enterprise Miner Workflow process, which consist of four different nodes that enable us to derive at our final findings
