Intelligent techniques for forecasting multiple time series in real‐world systems

Abstract

Purpose-The purpose of this paper is to describe a real-world system developed for a large food distribution company which requires forecasting demand for thousands of products across multiple warehouses. The number of different time series that the system must model and predict is on the order of 10 5. The study details the system's forecasting algorithm which efficiently handles several difficult requirements including the prediction of multiple time series, the need for a continuously self-updating model, and the desire to automatically identify and analyze various time series characteristics such as seasonal spikes and unprecedented events. Design/methodology/approach-The forecasting algorithm makes use of a hybrid model consisting of both statistical and heuristic techniques to fulfill these requirements and to satisfy a variety of business constraints/rules related to over-and under-stocking. Findings-The robustness of the system has been proven by its heavy and sustained use since being adopted in November 2009 by a company that serves 91 percent of the combined populations of Australia and New Zealand. Originality/value-This paper provides a case study of a real-world system that employs a novel hybrid model to forecast multiple time series in a non-static environment. The value of the model lies in its ability to accurately capture and forecast a very large and constantly changing portfolio of time series efficiently and without human intervention.

Model of medicines sales forecasting taking into account factors of influence

“…In previous work [13], the factors were studied which impact the sales of prescription drugs. The purpose of the study was to evaluate the effect of price and advertising on sales of medicines.…”

“…There was also a correlation between drugs prescription with gender and age of the doctor. However, the distribution indicators in [13] were not considered. Based on the authors' studies of the distribution of the drug, an original algorithm was developed for the method of constructing the forecast, taking into account the impact factors (figure 2).…”

Application Resource Management for Highly Computational Applications in the Operational Environment: A Critical Review

“…This means it should be able to understand its current environment of operation and the current circumstances, and the resources available [41]. Prediction and Forecasting should be important components of many real-world enterprise applications [42]. Such capabilities help in reducing the risk of making wrong decisions while allocating resources to transactions [9].…”

Forecasting Direction of Trend of a Group of Analogous Time Series Using F-Transform and Fuzzy Natural Logic

“…According to 24 , these models have been shown (i) to be extremely flexible in capturing the dynamic inter-relationships between a set of variables, (ii) to be able to treat several variables endogenously, (iii) not to require firm prior knowledge on the nature of the different relationships, (iv) to be able to capture both short-and long-run inter-relationships, and (v) to outperform multivariate TSA models in parameter efficiency, goodness-of-fit measures as well as in forecasting performance.…”