Abstract
This paper proposes using Zipf-like distribution to balance and predict the quantity of business in finance shared service center. It can estimate prediction parameters according to the present statistics of the business quantity occurrence. We classify the quantity of business from finance shared service center, and analyze the prediction rate through the quantity of business’s characteristic. We synthesize the analysis results in different prediction time granularity and prediction business quantity queue. Finally, we use the actual data from finance shared service center to discuss the effectiveness of prediction method. The discussion and analysis results indicate that this prediction method can balance the quantity of business efficiently
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