The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network

Yang, Liansheng; Li, Baolin

doi:10.14257/ijdta.2016.9.1.06

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there are industries where seasonality can not be accurately predicted, since its occurrence is unpredictable. For example, predicting sales, the researchers determined the seasonal index of the print media industry (A. M. Garcia, G. Pak, F. Oduro, D. Thompson, K. Erazo and J. Gilkey Jr, 2017) [15], sales of vehicles (M. Sivak and B. Schoettle, 2017; L. Yang and B. Li, 2017) [16][17]. We think that the factor of seasonality has a significant impact on the process of sales of products in light industry.…”

Section: Resultsmentioning

confidence: 99%

Modeling of an effective strategy of the process of product implementation with reference to seasonality

Костишин¹,

Semchyshyn²,

Yakovets³

2018

EA-XXI

View full text Add to dashboard Cite

Introduction. It is impossible to solve actual economic problems of enterprises without building strategies and forecasting the results of their functioning. A well-established implementation process increases product demand, competitiveness, as well as improves the image of the company and financial stability in general. Particular attention should be paid to studying the impact of seasonality. In this context, special attention should be paid to the activity of enterprises that sell light industry products, such as blankets and plaids, because this industry is also impacted by seasonal fluctuations that a specific nature, which is a consequence of both changes in nature and human needs. Therefore, when considering the variability of consumer demand, the presence of a large number of enterprises of different forms of ownership, increasing uncertainty and the existence of many risks, it is important to form an effective implementation strategy, which is a general plan for achieving the aims. The purpose of the article is to analyse the impact of factors relating to the process of selling light industry products and propose a model of strategy to increase its efficiency, taking into account seasonality. Results. The authors of the research have formed an algorithmic model of construction of the implementation process with the consideration of the factors increasing its efficiency. The practical aspect of its application is given on the example of the enterprise with foreign capital, which sells products of light industry with regard to seasonality. The conducted analysis made it possible to determine the forecast amount of proceeds from sales of production for each quarter of 2019, as well as to define the lower and upper limits of the forecast. Conclusions. The application of the proposed model in order to forecast revenue from sales of enterprise products with regard to seasonality will make it possible to increase the efficiency of the implementation process, eliminate existing risks and streamline management of the specified process in certain quarters in the context of seasonal fluctuations.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling of an effective strategy of the process of product implementation with reference to seasonality

Костишин¹,

Semchyshyn²,

Yakovets³

2018

EA-XXI

View full text Add to dashboard Cite

show abstract

“…E-commerce platforms need to predict sales in order to optimize their advertising strategies, inventory management, and pricing strategies. Some e-commerce platforms using machine learning and time series analysis have achieved highly personalized product recommendations, resulting in improved conversion rates [13]. Pharmacies and healthcare organizations need to predict the demand for medications to ensure they always have enough inventory to meet patient needs.…”

Section: Applicationmentioning

confidence: 99%

Application of Big Data Analysis in Sales Forecasting

Ma,

Xie

2023

AEMPS

View full text Add to dashboard Cite

As a matter of fact, with the rapid development of the computation ability and data delivering, the big data analysis becomes a common tool to realize the predictions. With this in mind, this paper focuses on the impact of big data analysis on sales forecasting, starting with an introduction to the present-day context and significance of sales forecasting and some of the forecasting scenarios implemented by previous generations based on machine learning. After introducing these backgrounds, we start to introduce big data analysis and its impact on sales forecasting. According to the analysis and the summaty of the previous experience, we introduce the commonly used models, the parameters of the models, and the evaluation of the models as well as the application scenarios, and the specific application results. Finally, we demonstrate the limitations as well as future perspectives of big data analysis and sales forecasting. Overall, these results shed light on guiding further exploratio of bigdata analysis in sales prediction.

show abstract

“…The idea behind combining techniques is that each model's approach to identifying patterns is different, and combining the predictions of these single models to form the final forecast can provide advantages, as the combination can capture a broader cross section of patterns in the data (Zhang 2003). In light of the benefits that combined forecasts offer, many studies relating to retail sales forecasting have focused on methods involving the integration of some artificial intelligence techniques into neural networks, or on combining neural networks with traditional models, in order to improve forecasts (Chen, Ou 2011;Lu 2014;Du et al 2015;Yang, Li 2016). However, it should be kept in mind that using combining methods is not a panacea.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Retail Sales Forecasting Between Single and Combination Methods

Aras

Kocakoç

Polat

2017

Journal of Business Economics and Management

View full text Add to dashboard Cite

In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company’s current system.

show abstract

The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network

Abstract: Abstract

Cited by 4 publications

References 10 publications

Modeling of an effective strategy of the process of product implementation with reference to seasonality

Modeling of an effective strategy of the process of product implementation with reference to seasonality

Application of Big Data Analysis in Sales Forecasting

Comparative Study on Retail Sales Forecasting Between Single and Combination Methods

Contact Info

Product

Resources

About