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Medium and long term demand forecasting method for tendency and periodicity commodities

A demand forecasting and periodic technology, applied in forecasting, data processing applications, computing, etc.

Active Publication Date: 2014-03-05
中储南京智慧物流科技有限公司
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  • Abstract
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Problems solved by technology

[0003] Purpose of the invention: Aiming at the problems and deficiencies in the existing commodity transaction data forecasting, the present invention provides a commodity forecasting model and forecasting method based on the exponential smoothing method that decomposes the linear trend, seasonal variation and random variation time series

Method used

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  • Medium and long term demand forecasting method for tendency and periodicity commodities
  • Medium and long term demand forecasting method for tendency and periodicity commodities
  • Medium and long term demand forecasting method for tendency and periodicity commodities

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Embodiment Construction

[0044] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0045] Such as figure 1 As shown, the medium and long-term demand forecasting method for trendy and cyclical commodities includes the following steps:

[0046] (1) Obtain historical data. Obtain all historical data of previous commodity sales, arranged in chronological order.

[0047] (2) Build a commodity historical database and draw trend charts.

[0048] i. Create an Excel table, enter the historical information of commodity-related demand and supply into the database in chronological order ...

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Abstract

The invention discloses a medium and long term demand forecasting method for tendency and periodicity commodities. The method is suitable for medium and long term forecasting of the commodities which have periodicity, are influenced by trend variation and are sensitive to seasons. On the basis of exponential smoothing, linear trend, seasonal variation and random variation time series are decomposed, full consideration is given to influences of the trend variation and period variation on commodity transaction data, commodity transaction data according with a forecast model are selected according to a commodity data selection principle, corresponding smoothing parameters are determined, various indexes in the forecast model are respectively calculated through a quantitative method, and a predicted value of the commodities in a certain period in the future is obtained. Therefore, scientific judgments are provided for production, manufacturing, storage, marketing and the like of various enterprises in commodity supply chains.

Description

technical field [0001] The invention relates to a medium- and long-term demand forecasting method for trendy and cyclical commodities, which is suitable for medium- and long-term forecasting of seasonally sensitive commodities with periodic phenomena, affected by trend changes, and belongs to the technical field of information forecasting. Background technique [0002] With the development of the market economy, market development has deepened from the era of price competition to the era of cost competition. The preliminary judgment of the future trend of the market plays an increasingly important role in the operation and management of enterprises. To a large extent, it affects enterprises. The ability to recognize opportunities and seize them. Currently, the processing of commodity historical data stays in the construction of time series and simple trend lines of historical sales. However, since the trend line obtained by such a method contains multiple factors such as sp...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q10/04
Inventor 李敬泉
Owner 中储南京智慧物流科技有限公司
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