Seasonal commodity demand prediction method based on time sequence decomposition

Abstract

The invention discloses a seasonal commodity demand prediction method based on time sequence decomposition. The method comprises the following steps: firstly, separating a peak sequence s1 and a conventional value sequence s2 from historical demand data based on a statistical method; secondly, based on the peak sequence s1, marking whether the training data is a peak demand or not; predicting a peak occurrence probability p by using a composite classifier consisting of two classifiers; and calculating a peak probability threshold value alpha by using recent historical data, carrying out regression strategy selection based on the peak prediction probability p and the peak probability threshold value, if p is greater than alpha, carrying out peak demand prediction by using a K neighbor model, otherwise, carrying out non-peak demand regression prediction by using a random forest model. According to the method, through seasonal peak probability modeling, seasonal demands are predicted by utilizing a plurality of regression models, sudden seasonal commodity peak values are effectively coped with, meanwhile, the accuracy of peak value prediction is greatly improved, and favorable supportis provided for enterprises to purchase seasonal commodities.

Description

technical field [0001] The invention belongs to the technical field of information forecasting and provides a seasonal commodity demand forecasting method based on time series decomposition. A mid-to-long-term forecasting method suitable for seasonal materials whose outbound data presents periodic changes. Background technique [0002] Demand forecasting not only involves customer demand management, but also plays a leading role in subsequent operations such as ordering and inventory, directly affecting the increase in corporate profits. The demand forecast is too high, the inventory backlog increases the inventory cost of the enterprise, which is not conducive to the capital turnover of the enterprise; the demand forecast is too low, which cannot meet the current customer demand, resulting in the loss of customers. Therefore, demand forecasting is a major challenge faced by most enterprises, especially manufacturing and retail industries, in supply chain management. [00...

AI Technical Summary

Problems solved by technology

In practice, the demand to be predicted is usually the lead time demand, so the calculation cycle is longer, and the SARIMAX model is not applicable

In addition, the three-time exponential smoothing model that decomposes the time series into trends and seasonal disturbances must be judged whether the trend / seasonal disturbances are additive or multiplicative before use, and it is difficult to implement when processing multiple materials at the same time

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