ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting

Abstract

In forecasting, evolutionary algorithms are often linked to existing forecasting methods to optimize their input parameters. Traditionally, the fitness function of these search heuristics is based on an accuracy measure. In this paper, however, we combine forecasting accuracy with business expertise by defining a flexible and easily interpretable profit function for sales forecasting, which is based on the profit margin of a given product, the volume of its sales and the accuracy of the forecast. ProfARIMA is a new procedure that selects the lags of a Seasonal ARIMA model according to the profit of a model's forecasts by taking advantage of search heuristics. This procedure is tested on both publicly available datasets and a real-life application with datasets of The Coca-Cola Company in order to assess its performance, both in profit and accuracy. Three different evolutionary algorithms were implemented during this testing process, i.e. Genetic Algorithms, Particle Swarm Optimization and Simulated Annealing. The results indicate that ProfARIMA always performs at least equally to the Box-Jenkins methodology and often outperforms this traditional procedure. For The Coca-Cola Company, our new algorithm in combination with Genetic Algorithms even leads to a significantly larger profit for out-of-sample forecasts.

Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

“…They presented that ARIMA obtains good performance in one-step and multi-step forecasting. Van Calster et al [19] developed ProfARIMA which handles the lags of a seasonal ARIMA model. They applied a developed model to the sales data of the Coca-Cola company and showed its accuracy.…”

Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

“…The study found that ARIMA methods can be effectively combined with soft computing strategies to enhance the precision of energy forecasting model. The work in [24] has also proposed the usage of an ARIMA technique for predicting Greek electricity utilization where the planned technique in comparison with three systematic time-series methods showed better results. time series model is further proposed in [30] for short term hourly prediction for peak loads using an improved ARIMA model where the prediction results show better results over the original model.…”

“…The Autoregressive Integrated Moving Average (ARIMA) [21] is a statistical liner model for time series prediction which has been shown to be suitable for modelling short-term forecasting and has been utilized in an assortment of applications such as predicting energy demand [21], wind speed forecasting [22], vehicular traffic flow prediction [23] and sales forecasting [24]. The ARIMA model is a popular time series prediction model [25] and is well suited for monthly consumption [26] forecasting of energy usage.…”

Forecasting of Chinese E-Commerce Sales: An Empirical Comparison of ARIMA, Nonlinear Autoregressive Neural Network, and a Combined ARIMA-NARNN Model

“…The prediction studies of ARIMA and NARNN models in other areas [2][3][4][5][6][7] found that ARIMA fits and forecasts better when the time series data shows a clear linear trend, otherwise the prediction becomes less accurate or even lower than the confidence requirements, while the NARNN model shows a better prediction performance for nonlinear changes in the data.…”

“…In fact, ARIMA, NARNN, and ARIMA-NARNN have been studied in many industries, such as agriculture and forestry [2], healthcare [3,5], geography [4], manufacturing [6], and offline retail [7]. Some of these studies [2][3][4][5] only analyzed a single time series to reach conclusions, and some [6,7] only conducted empirical analysis of the hybrid model and did not compare the ARIMA, NARNN, and ARIMA-NARNN to prove the effectiveness of the hybrid model. The innovativeness of this paper is to do a comparative study between the ARIMA, NARNN, and ARIMA-NARNN methods combining the characteristics of the e-commerce industry.…”