Commodity sales forecast based on ARIMA model residual optimization

Accurate merchandise sales forecasts increase the turnover rate for merchants' benign inventory stocking, formulate cost budget items, formulate effective strategies according to the time and local conditions, and provide theoretical basis for reasonable merchandise sales strategies. However, the traditional methods of forecasting are not comprehensive and the actual effect is not ideal. Use the ARIMA model to fit the original product sales time series, extract the main deterministic information in the product sales time series through the deterministic factor decomposition method, and subtract or remove the trend effect fitting polynomial and seasonal effect fitting from the sales time series The remaining part of the polynomial obtains the residual sequence. The similarity distance function of the comparison layer in the adaptive resonance theory neural network is improved to measure the degree of similarity between a subsequence of the residual sequence and another subsequence, and the weight is adjusted according to the similarity. Each neuron in the competition layer of the network compares the input subsequences accordingly to generate the most similar subsequences of each subsequence, and iteratively eliminates all sequences related to the total residual sequence to obtain the final residual sequence that meets the requirements. Experimental comparison shows that the model after optimization by residual error can reflect the sales law more accurately and comprehensively, and improve the accuracy of product sales forecast.