• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.533-543
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• 2023

This study explores the influence of the COVID-19 pandemic on the Korean art market and contrasts the classic hedonic method of art price prediction with the Artificial Neural Network technique. The empirical analysis of this paper utilizes 14,639 observations of Korean art auction data from 2015 to 2021. There are three types of variables in this study: artist-related, artwork-related, and sales-related. Previous studies have suggested that these three types of variables influence art prices. The empirical findings in this research are in twofold. First, in terms of RMSE and R², the Artificial Neural Network outperforms the hedonic model. Both techniques discover that sales and artwork variables have a greater impact than artist-related attributes. Second, when the primary factors of art price are controlled, Korean art prices are found to fall dramatically in 2020, shortly following the onset of COVID-19, but to rebound in 2021. The main lesson in this study is that the Artificial Neural Network enhances art price prediction and reduces information asymmetry in the Korean art market even in the face of unanticipated turmoil such as the COVID-19 outbreak.

• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.211-229
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• 2004

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

• Fisheries and Aquatic Sciences
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• v.15 no.4
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• pp.345-352
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• 2012

To predict catches of Pacific anchovy Engraulis japonicus larvae, anchovy eggs were collected in the coastal waters off Gunsan, Korea, in the Yellow Sea during the main spawning season (June to July) from 2003 to 2009. A ring net was repeatedly towed vertically at 10 stations during the daytime to sample eggs. Catch data estimated by auction sales were obtained from the Fisheries Cooperatives Union of Gunsan City and daily water temperature data in the outer harbor of Gunsan City during the survey periods were obtained from the National Oceanographic Research Institute. A significant relationship was found between anchovy egg density from June to July and larval catch from July to October in the same year. Catch of anchovy larvae in Gunsan were also high when optimal growth temperatures were recorded in the coastal waters off Gunsan in July. Although the recruitment success or failure of anchovy larvae can be predicted from variability in egg density, we suggest that mean daily water temperature is a more efficient indicator for predicting variability in catches of larval anchovy in the Yellow Sea.

• Journal of Distribution Science
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• v.19 no.11
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• pp.49-57
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• 2021

Purpose: In this study, we try to figure out the appropriate size of commercial districts in the original downtown area through empirical studies targeting the Jinju Central Commercial Area in Gyeongnam and Cheonan Station in Chungnam, which are trying to regenerate a specific space that has been lost through government projects. Research design, data and methodology: The current status and characteristics of the shopping district were examined through on-site surveys of the central business district of Jinju, Gyeongnam Province, and Cheonan Station, Chungnam Province, and the size of the empty stores was determined. In addition, the standard median income was used as the survey data along with the survey of the mobile population in the commercial area. Result: The analysis result shows that 883 stores should be maintained considering the overall expenditure and gross sales profit within Cheonan Station in South Chungcheong Province. Currently, considering spending and margins in the Commercial Area, Jinju Central Commercial Area is a place where 222 stores can be sold excessively, and a proper commercial supply plan is needed. Conclusions: In this study, we conducted a demand prediction study in the commercial sector of the most basic sector to regenerate the commercial sector through major regional commercial districts.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.43-55
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• 2016

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.155-160
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• 2014

When we collect marketing data, we can only obtain the bimonthly or quarterly data but the monthly data be available. If we evaluate or predict monthly market condition or establish monthly marketing strategies, we need to disaggregate these bimonthly or quarterly data to the monthly data. In this paper, for bimonthly or quarterly data, we introduce some methods of disaggregation to monthly data. These disaggregation methods include the simple average method, the growth rate method, the weighting method by the judgment of experts, and variable decomposition method using 12 month moving cumulative sum. In this paper, we applied variable decomposition method to disaggregate for bimonthly data of sum of electronics sales in a European country. We, also, introduce how to use this method to predict the future data.