• Title/Summary/Keyword: Demand Forecasting Model

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Forecasting Demand of 5G Internet of things based on Bayesian Regression Model (베이지안 회귀모델을 활용한 5G 사물인터넷 수요 예측)

  • Park, Kyung Jin;Kim, Taehan
    • Journal of Information Technology Applications and Management
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    • v.26 no.2
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    • pp.61-73
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    • 2019
  • In 2019, 5G mobile communication technology will be commercialized. From the viewpoint of technological innovation, 5G service can be applied to other industries or developed further. Therefore, it is important to measure the demand of the Internet of things (IoT) because it is predicted to be commercialized widely in the 5G era and its demand hugely effects on the economic value of 5G industry. In this paper, we applied Bayesian method on regression model to find out the demand of 5G IoT service, wearable service in particular. As a result, we confirmed that the Bayesian regression model is closer to the actual value than the existing regression model. These findings can be utilized for predicting future demand of new industries.

Estimation of Dynamic Effects of Price Increase on Sales Using Bayesian Hierarchical Model (베이지안 다계층모형을 이용한 가격인상에 따른 판매량의 동적변화 추정 및 예측)

  • Jeon, Deok-Bin;Park, Seong-Ho
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2005.05a
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    • pp.798-805
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    • 2005
  • Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expect it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. Above factors make the sales dynamic and unstable. We develop a time series model to evaluate the sales patterns with stockpiling and short term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods.

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A review of artificial intelligence based demand forecasting techniques (인공지능 기반 수요예측 기법의 리뷰)

  • Jeong, Hyerin;Lim, Changwon
    • The Korean Journal of Applied Statistics
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    • v.32 no.6
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    • pp.795-835
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    • 2019
  • Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

The Development of Travel Demand Nowcasting Model Based on Travelers' Attention: Focusing on Web Search Traffic Information (여행자 관심 기반 스마트 여행 수요 예측 모형 개발: 웹검색 트래픽 정보를 중심으로)

  • Park, Do-Hyung
    • The Journal of Information Systems
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    • v.26 no.3
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    • pp.171-185
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    • 2017
  • Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

A Study on the New Product Forecasting Methodology (신제품 수요예측 방법론 연구)

  • Lim, Jong-In;Oh, Hyung-Sik
    • Journal of Korean Institute of Industrial Engineers
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    • v.18 no.2
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    • pp.51-63
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    • 1992
  • It is commonly accepted that the demand forecasting data play a vital role in deciding strategic variables such as the optimal market entry time, the price structure and the production capacity etc. In case of the new product, however, it is hard to apply the well known regression-type methodologies. In this study, we survey the characteristics of various types of new product demand forecasting(NPDF) methodologies which are useful in case the historical data are not available. Further, we explore the possibility of incorporating the NPDF methodologies and develope the unified infra-structure of the NPDF methodologies. Finally we propose an integrated prototype of the NPDF model.

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A Study on the Electric System Design by the Forecasting of Maximum Demand (최대수요전력 예측에 의한 전기계통 설계에 관한 연구)

  • 황규태;김수석
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.6 no.1
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    • pp.29-39
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    • 1992
  • In this paper, the basic idea of optimum electric system design by means of the forecasting of maximum demand is presented, and the load characteristics and practical operating conditions are based on the technical data. After reconstruction of th model plant by use of above method, power supply reliability, future extention, initial cost, and running cost saving effects are analyzed. As a result, it is verified that the systems wherein the power is supply to each load frm main transformer whose capacity is calculated by forecasting are economic rather than the systems wherein the power is supply to each electric feeders from each corresponding transformer.

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Supercomputing Performance Demand Forecasting Using Cross-sectional and Time Series Analysis (횡단면분석과 추세분석을 이용한 슈퍼컴퓨팅 성능수요 예측)

  • Park, Manhee
    • Journal of Technology Innovation
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    • v.23 no.2
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    • pp.33-54
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    • 2015
  • Supercomputing performance demand forecasting at the national level is an important information to the researchers in fields of the computational science field, the specialized agencies which establish and operate R&D infrastructure, and the government agencies which establish science and technology infrastructure. This study derived the factors affecting the scientific and technological capability through the analysis of supercomputing performance prediction research, and it proposed a hybrid forecasting model of applying the super-computer technology trends. In the cross-sectional analysis, multiple regression analysis was performed using factors with GDP, GERD, the number of researchers, and the number of SCI papers that could affect the supercomputing performance. In addition, the supercomputing performance was predicted by multiplying in the cross-section analysis with technical progress rate of time period which was calculated by time series analysis using performance(Rmax) of Top500 data. Korea's performance scale of supercomputing in 2016 was predicted using the proposed forecasting model based on data of the top500 supercomputer and supercomputing performance demand in Korea was predicted using a cross-sectional analysis and technical progress rate. The results of this study showed that the supercomputing performance is expected to require 15~30PF when it uses the current trend, and is expected to require 20~40PF when it uses the trend of the targeting national-level. These two results showed significant differences between the forecasting value(9.6PF) of regression analysis and the forecasting value(2.5PF) of cross-sectional analysis.

A Study on Artificial Intelligence Model for Forecasting Daily Demand of Tourists Using Domestic Foreign Visitors Immigration Data (국내 외래객 출입국 데이터를 활용한 관광객 일별 수요 예측 인공지능 모델 연구)

  • Kim, Dong-Keon;Kim, Donghee;Jang, Seungwoo;Shyn, Sung Kuk;Kim, Kwangsu
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.35-37
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    • 2021
  • Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.

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Comparative Evaluation of Diffusion Models using Global Wireline Subscribers (세계 유선인터넷 서비스에 대한 확산모형의 예측력 비교)

  • Min, Yui Joung;Lim, Kwang Sun
    • Journal of Information Technology Applications and Management
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    • v.21 no.4_spc
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    • pp.403-414
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    • 2014
  • Forecasting technology in economic activity is a quite intricate procedure so researchers should grasp the point of the data to use. Diffusion models have been widely used for forecasting market demand and measuring the degree of technology diffusion. However, there is a question that a model, explaining a certain market with goodness of fit, always shows good performance with markets of different conditions. The primary aim of this paper is to explore diffusion models which are frequently used by researchers, and to help readers better understanding on those models. In this study, Logistic, Gompertz and Bass models are used for forecasting Global Wireline Subscribers and the performance of models is measured by Mean Absolute Percentage Error. Logistic model shows better MAPE than the other two. A possible extension of this study may verify which model reflects characteristics of industry better.

Constrained NLS Method for Long-term Forecasting with Short-term Demand Data of a New Product (제약적 NLS 방법을 이용한 출시 초기 신제품의 중장기 수요 예측 방안)

  • Hong, Jungsik;Koo, Hoonyoung
    • Journal of the Korean Operations Research and Management Science Society
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    • v.38 no.1
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    • pp.45-59
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    • 2013
  • A long-term forecasting method for a new product in early stage of diffusion is proposed. The method includes a constrained non-linear least square estimation with the logistic diffusion model. The constraints would be critical market informations such as market potential, peak point, and take-off. Findings on 20 cases having almost full life cycle are that (i) combining any market information improves the forecasting accuracy, (ii) market potential is the most stable information, and (iii) peak point and take-off information have negative effect in case of overestimation.