| 主权项 |
1. A computer implemented method comprising:
accessing website visitor data, the website visitor data comprising a plurality of time-series intervals, wherein each of the time-series intervals reflect a period of time that a website is available to receive visitors; determining, by at least one processor and using a cost function, a cost value for each time-series interval in the plurality of time-series intervals, wherein the cost function compares website visitor data from at least two time-series intervals; organizing, by the at least one processor, the determined cost values into multiple cost value clusters using a clustering algorithm; identifying at least one cost value cluster from the multiple cost value clusters by identifying at least one typical cost value cluster based on user input; identifying, by the at least one processor, one or more anomalous clusters from the multiple cost value clusters, the one or more anomalous clusters representing at least one anomaly in the website visitor data; modifying at least one cost value corresponding to the at least one anomaly in the website visitor data based on the at least one cost value cluster, wherein modifying the at least one cost value comprises:
generating a mean cost value from the at least one typical cost value cluster, andreplacing the at least one cost value corresponding to the at least one anomaly with the mean cost value; creating, by the at least one processor, a set of normalized data from the at least one modified cost value by applying the cost function to the modified at least one cost value; predicting website visitor data for a future time-series interval based on the set of normalized data; and selecting, by the at least one processor, a digital advertisement for the website using the predicted website visitor data. |
