• 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.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1480-1491
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• 2015

In new deregulated electricity market, short-term price forecasting is key information for all market players. A better forecast of market-clearing price (MCP) helps market participants to strategically set up their bidding strategies for energy markets in the short-term. This paper presents a new prediction strategy to improve the need for more accurate short-term price forecasting tool at spot market using an artificial neural networks (ANNs). To build the forecasting ANN model, a three-layered feedforward neural network trained by the improved Levenberg-marquardt (LM) algorithm is used to forecast the locational marginal prices (LMPs). To accurately predict LMPs, actual power generation and load are considered as the input sets, and then the difference is used to predict price differences in the spot market. The proposed ANN model generalizes the relationship between the LMP in each area and the unconstrained MCP during the same period of time. The LMP calculation is iterated so that the capacity between the areas is maximized and the mechanism itself helps to relieve grid congestion. The addition of flow between the areas gives the LMPs a new equilibrium point, which is balanced when taking the transfer capacity into account, LMP forecasting is then possible. The proposed forecasting strategy is tested on the spot market of the Nord Pool. The validity, the efficiency, and effectiveness of the proposed approach are shown by comparing with time-series models

• Journal of Animal Environmental Science
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• v.6 no.1
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• pp.15-22
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• 2000

A monitoring and the forecasting system for the swine delivery was developed using CCD camera multi-function board microphone and data-recorder equipped on a personal computer. For the swine delivery monitoring and forecasting factors four factors were selected such as genitalia swine body shape breast color and sound. Image of physical variation of body shape, shape and color of genitalia area and color of breast of pregnant sow were grabbed using the CCD color camera and multi-function board and variation of voice of pregnant sow was acquired using microphone and data recorder. Acquired information of image and voice were analyzed using a custom developed algorithm and program. The result of the forecasting efficiency of swine delivery was 89%, 71% and 100% using the variation of genitalia are the body shape and the voice of pregnant sow. respectively. The efficiency of image processing was 100% for the delivery detection when the piglet was delivered half of its body from genitalia of pregnant sow, The monitoring and forecasting system informed the estimated time of the delivery of swine to a farm manager immediately if an estimated and established time set by the farm manager was the same and/or the estimated time ws earlier than the established time and the system detected the delivery.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.11-25
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• 2021

The objective of this study was to analyze the impact of activation functions on flood forecasting model based on Artificial neural networks (ANNs). The traditional activation functions, the sigmoid and tanh functions, were compared with the functions which have been recently recommended for deep neural networks; the ReLU, leaky ReLU, and ELU functions. The flood forecasting model based on ANNs was designed to predict real-time runoff for 1 to 6-h lead time using the rainfall and runoff data of the past nine hours. The statistical measures such as R2, Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), the error of peak time (ETp), and the error of peak discharge (EQp) were used to evaluate the model accuracy. The tanh and ELU functions were most accurate with R2=0.97 and RMSE=30.1 (㎥/s) for 1-h lead time and R2=0.56 and RMSE=124.6~124.8 (㎥/s) for 6-h lead time. We also evaluated the learning speed by using the number of epochs that minimizes errors. The sigmoid function had the slowest learning speed due to the 'vanishing gradient problem' and the limited direction of weight update. The learning speed of the ELU function was 1.2 times faster than the tanh function. As a result, the ELU function most effectively improved the accuracy and speed of the ANNs model, so it was determined to be the best activation function for ANNs-based flood forecasting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.2
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• pp.89-103
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• 2013

National demands for the ocean forecasting system have been increased to support economic activity and national safety including search and rescue, maritime defense, fisheries, port management, leisure activities and marine transportation. Further, the ocean forecasting has been regarded as one of the key components to improve the weather and climate forecasting. Due to the national demands as well as improvement of the technology, the ocean forecasting systems have been established among advanced countries since late 1990. Global Ocean Data Assimilation Experiment (GODAE) significantly contributed to the achievement and world-wide spreading of ocean forecasting systems. Four stages of GODAE were summarized. Goal, vision, development history and research on ocean forecasting system of the advanced countries such as USA, France, UK, Italy, Norway, Australia, Japan, China, who operationally use the systems, were examined and compared. Strategies of the successfully established ocean forecasting systems can be summarized as follows: First, concentration of the national ability is required to establish successful operational ocean forecasting system. Second, newly developed technologies were shared with other countries and they achieved mutual and cooperative development through the international program. Third, each participating organization has devoted to its own task according to its role. In Korean society, demands on the ocean forecasting system have been also extended. Present status on development of the ocean forecasting system and long-term plan of KMA (Korea Meteorological Administration), KHOA (Korea Hydrographic and Oceanographic Administration), NFRDI (National Fisheries Research & Development Institute), ADD (Agency for Defense Development) were surveyed. From the history of the pre-established systems in other countries, the cooperation among the relevant Korean organizations is essential to establish the accurate and successful ocean forecasting system, and they can form a consortium. Through the cooperation, we can (1) set up high-quality ocean forecasting models and systems, (2) efficiently invest and distribute financial resources without duplicate investment, (3) overcome lack of manpower for the development. At present stage, it is strongly requested to concentrate national resources on developing a large-scale operational Korea Ocean Forecasting System which can produce open boundary and initial conditions for local ocean and climate forecasting models. Once the system is established, each organization can modify the system for its own specialized purpose. In addition, we can contribute to the international ocean prediction community.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.10
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• pp.126-133
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• 2007

This paper proposes information technology based data mining to forecast short term power demand. A time-series analyses have been applied to power demand forecasting, but this method needs not only heavy computational calculation but also large amount of coefficient data. Therefore, it is hard to analyze data in fast way. To overcome time consuming process, the author take advantage of universally easily available information technology based data-mining technique to analyze patterns of days and special days(holidays, etc.). This technique consists of two steps, one is constructing decision tree, the other is estimating and forecasting power flow using decision tree analysis. To validate the efficiency, the author compares the estimated demand with real demand from the Korea Power Exchange.