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.

Forecasting port container throughput is crucial due to its impact on economic development. Socio-economic factors, which introduce uncertainty, are increasingly integrated into throughput forecasting. The complexity of common multivariate forecasting models significantly affects accuracy, yet few studies compare their performance on the same time series for throughput modeling. This study implements, evaluates, and compares the performance of eight multivariate forecasting models for port throughput within a proposed multiple-input single-output (MISO) system, chosen for their frequent use in container throughput research. It investigates two data preprocessing approaches: Random Forest Variable Importance Method (RF-VIM) and a Multi Lagged Value approach. The comparison uses six error metrics: normalized root mean squared error, mean absolute error, mean absolute percentage error, mean error, and root mean percentage error. Performances are discussed, and recommendations for adopting a suitable model are provided.

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies – Bitcoin, Ethereum, Litecoin, and EOS – and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies – AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet – representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning- based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

본 연구에서는 레이더 관측 영역 내에 강수 에코(echo)가 없는 지역을 비강수 정보라고 정의하고 자료 동화에 활용하였다. 비강수 정보는 레이더로 관측할 수 있는 최대 영역 내에서 강수에 의한 에코가 나타나지 않고 레이더에서 관측할 수 없을 정도로 약한 강수나 구름 입자가 있거나, 강수 자체가 없다는 것을 의미한다. 기존의 레이더 자료를 동 화한 연구가 강수에 의한 반사도와 시선속도를 동화하여 모델 내의 강수를 만들어내는 것에 초점을 두었다면, 본 연구 에서는 에코가 없다는 것도 하나의 정보로 고려하고 이를 동화함으로써 모델 내에서 잘못 예측한 강수를 억제하였다. 비강수 정보를 자료동화에 적용시키기 위해 레이더 비강수 정보를 수상체와 상대습도로 변환하는 관측 연산자를 제시 하고 이를 Weather Research and Forecasting (WRF) 모델의 자료동화 시스템인 WRF Data Assimilation system (WRFDA)에 적용하였다. 또한 비강수 정보를 효과적으로 활용하기 위한 레이더 자료의 처리 방법을 제시하였다. 비강 수 정보가 모델 내에서 잘못 예측한 강수를 억제할 수 있는지 확인하기 위해 단일 관측실험을 수행하였으며 비강수 정 보가 수상체와 습도 그리고 기온을 낮춤으로써 대류가 억제될 수 있는 환경을 만들었다. 비강수 정보의 동화 효과를 실제 사례에 적용한 2013년 7월 23일 대류 사례 실험을 통해 9시간 예측을 수행하여 결과를 분석하였다. 레이더 비강 수 정보를 추가로 동화한 실험이 비강수 정보를 제외한 실험보다 Fractional Skill Score (FSS)가 증가하고 False Alarm Ratio (FAR)는 감소하여 모델의 강수 예측성을 향상시켰다.

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea’s SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

The objective of this study is to compare the efficiency of VR and 2D in the tourism industry as marketing tools, using affective forecasting and purchase intention. Accordingly, this study has two primary research aims. The first is to examine if a higher level of mental imagery (resulting from VR) is more effective than a lower level (2D) in a tourism marketing context. To evaluate this, the researchers use experimental method, measuring predicted dominance, predicted pleasure and predicted arousal, as well as purchase intention towards the hotel. Relevant to the tourism industry, tourism products are spatially and temporally distant (vs. near). This study aims to investigate how differently mental imagery, resulting from VR (versus 2D) experience, generates affective forecasting of a tourism product when tourists plan distant (versus near) future trips (temporal distance).

The current study, which consisted of two independent studies (laboratory and greenhouse), was carried out to project the hypothesis fungi-spray scheduling for leaf mold and gray leaf spot in tomato, as well as to evaluate the effect of temperature and leaf wet duration on the effectiveness of different fungicides against these diseases. In the first experiment, tomato leaves were infected with 1 × 104 conidia·mL-1 and put in a dew chamber for 0 to 18 hours at 10 to 25°C (Fulvia fulva) and 10 to 30°C (Stemphylium lycopersici). In farm study, tomato plants were treated for 240 hours with diluted (1,000 times) 30% trimidazole, 50% polyoxin B, and 40% iminoctadine tris (Belkut) for protection of leaf mold, and 10% etridiazole + 55% thiophanate-methyl (Gajiran), and 15% tribasic copper sulfate (Sebinna) for protection of gray leaf spot. In laboratory test, leaf condensation on the leaves of tomato plants were emerged after 9 hrs. of incubation. In conclusion, the incidence degree of leaf mold and gray leaf spot disease on tomato plants shows that it is very closely related to formation of leaf condensation, therefore the incidence of leaf mold was greater at 20 and 15°C, while 25 and 20°C enhanced the incidence of gray leaf spot. The incidence of leaf mold and gray leaf spot developed 20 days after inoculation, and the latency period was estimated to be 14‒15 days. Trihumin fungicide had the maximum effectiveness up to 168 hours of fungicides at 12 hours of wet duration in leaf mold, whereas Gajiran fungicide had the highest control (93%) against gray leaf spot up to 144 hours. All the chemicals showed an around 30‒50% decrease in effectiveness after 240 hours of treatment. The model predictions in present study could be help in timely, effective and ecofriendly management of leaf mold disease in tomato.

The water consumption data of individual consumers must be analyzed and forecast to establish an effective water demand management plan. A k-mean cluster model that can monitor water use characteristics based on hourly water consumption data measured using automated meter reading devices and demographic factors is developed in this study. In addition, the quantification model that can estimate the daily water consumption is developed. K-mean cluster analysis based on the four clusters shows that the average silhouette coefficient is 0.63, also the silhouette coefficients of each cluster exceed 0.60, thereby verifying the high reliability of the cluster analysis. Furthermore, the clusters are clearly classified based on water usage and water usage patterns. The correlation coefficients of four quantification models for estimating water consumption exceed 0.74, confirming that the models can accurately simulate the investigated demographic data. The statistical significance of the models is considered reasonable, hence, they are applicable to the actual field. Because the use of automated smart water meters has become increasingly popular in recent year, water consumption has been metered remotely in many areas. The proposed methodology and the results obtained in this study are expected to facilitate improvements in the usability of smart water meters in the future.

최근 급격한 변화를 겪고 있는 자율주행 자동차 분야의 미래 기술 및 시장 전 망 예측에 대한 요구와 관심이 집중되고 있다. 자동차 산업의 특성상, 복합적 요인의 상관관 계가 미치는 영향력이 크고 요인 간의 복잡도가 높으므로, 체계적인 미래 예측 방법론 적용 을 통한 미래 전망분석 및 전략 수립이 시급하다. 본 연구에서는 자동차 분야에 적합한 미래 예측 방법론 중 필드 변칙 완화기법(Field Anomaly Relaxation)과 다중관점 개념 기법 (Multiple Perspective Concept)을 복합적으로 적용하여, 자율주행 자동차 분야의 핵심기술 및 산업 동향에 관한 미래 시나리오들을 개발하여 실증하였다. 도출된 3개의 시나리오는 전 문가 평가 체크리스트를 통하여 타당성을 검증하였다. 본 연구 결과는 자율주행 자동차 산업 과 같은 다양한 변동성이 존재하는 분야의 미래 예측 방법 중 한 가지로 적용될 수 있다는 점에 의의가 있다.

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil’s inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

Seaports play a huge role in Vietnam's economy, being a border gate for economic and cultural exchanges with outsiders, especially the role of goods circulation. Container cargoes are one of the types of goods with large and increasing volume through Vietnam seaports. However, the heterogeneity between the seaport and the connected infrastructure greatly affects the capacity and efficiency of port investment. This is also one of the main causes leading to a shortage of goods, excess ports in some port areas. The root cause is that the planning has not kept up with the growth of the amount of goods arriving at the port, because the issue of forecasting the volume of goods through the port is not accurate. Therefore, it is necessary to develop models of forecasting container cargo through the ports with general, scientific, and high accuracy to serve the strategy, planning and development of seaport system; the work of planning and investment in the development of seaports, shipping fleets and other auxiliary transport infrastructure works. The purpose of this study is to build suitable forecasting models with high accuracy and reliability on the total volume of container cargo throughput of the Vietnamese seaport system. Based on the methods of a statistical survey, synthesis, regression analysis, and correlation to evaluate the influence of factors on container cargo volume through Vietnam's seaports in the period of 2004-2019. By incorporating more economic factors into the regression model, the paper focuses on forecasting container cargo through the Vietnamese seaport systems, going into cargo-based forecasting in tons and TEUs. The results of this study contribute to complete the rationale for forecasting, especially forecasts related to the shipping industry and the forecast for container cargo throughput of the seaport. Finally, selecting models for forecasting container cargo volume throughput of seaports by Vietnamese conditions.