Summary: Effective forecasting of the actual revenue from daily electricity sales of power companies is the key to strengthen the efficient operation of the stock funds and to schedule the cash flow budget on a daily basis. Because of the obvious seasonal influence of residential users’ electricity consumption, the high frequency of air conditioning usage in winter and summer will lead to relatively more electricity consumption in these two seasons. There is also a significant difference between weekend and non-weekend payment behavior (weekend effect). At the same time, due to the different payment modes and behaviors of users, especially the difference of the payment time, the arrival time of the payment will fluctuate greatly in each month, which makes it difficult to forecast the daily cash flow. In view of the above problems, the periodic and nonstationary characteristics of data structure are discussed by using piecewise multi-order differential nonstationary time series.