Timeline for Anomaly detection for transaction data
Current License: CC BY-SA 3.0
4 events
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| Apr 13, 2017 at 12:50 | history | edited | CommunityBot |
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| Feb 14, 2017 at 16:03 | comment | added | Hobbes | I would avoid any time series approach that specifically addresses seasonality. Start simple and work your way up. Some quick boxplots to visualize your data, and see if this captures your outliers. ESD (itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm) could be a useful method. Clustering could also work, and it is unsupervised, however, there are some pitfalls to k-means. Without knowing the data, it is hard to say what time series approaches will be appropriate but it's definitely worth a look. | |
| Feb 14, 2017 at 7:21 | comment | added | Kira | There is no seasonal data. Transactions are uniform. There is no training data. Would time-series work fine? | |
| Feb 13, 2017 at 15:32 | history | answered | Hobbes | CC BY-SA 3.0 |