## Volume Forecasting(볼륨 예측)란 무엇입니까?

Volume Forecasting 볼륨 예측 - Then, we introduce a multi-task prediction architecture to guide the sales volume forecasting and the seller predicting task.^{[1]}Accurate short-term traffic volume forecasting has become a component with growing importance in traffic management in intelligent transportation systems (ITS).

^{[2]}We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets.

^{[3]}Originality/valueAs the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study.

^{[4]}Combining with the relevant theories of tourism flow, this paper innovatively puts forward the traffic volume forecasting method of tourism expressway.

^{[5]}Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression.

^{[6]}Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy.

^{[7]}A comparison of results revealed that the SSA–ANN hybrid model could improve the forecasting accuracy of the conventional ANN model in the case of daily traffic volume forecasting.

^{[8]}Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume.

^{[9]}Short-term traffic volume forecasting is one of the most essential elements in Intelligent Transportation System (ITS) by providing prediction of traffic condition for traffic management and control applications.

^{[10]}The purpose of this study is to develop a model for traffic volume forecasting of the road network in Anamorava Region.

^{[11]}ABSTRACT Tourist volume forecasting is an ongoing theme in tourism research.

^{[12]}First, we transform the regression problem of traffic volume forecasting into a binary classification problem.

^{[13]}This paper proposes an effective freight volume forecasting approach for river-sea direct transport without direct historical data.

^{[14]}OBJECTIVES We sought to determine whether addition of a snowfall variable improves emergency department (ED) patient volume forecasting.

^{[15]}The paper presents the results of qualitative research carried out in the aspect of traffic volume forecasting on selected national roads, supported by a scientific search and discourse on logistic aspects of traffic management, with particular emphasis on Intelligent Transport Systems, in order to verify the effectiveness of the implementation of neural networks.

^{[16]}Therefore, the objective of this research is to analyze the potential impact of input variables uncertainty on the performance of sewage volume forecasting model.

^{[17]}This paper develops a multi-step approach framework for freight volume forecasting of RSDT in the case that direct historical data are not available.

^{[18]}This study tests whether Google search data and relevant statistical methods can improve the accuracy of ED volume forecasting compared with traditional data sources.

^{[19]}The present paper provides a comparative evaluation of hybrid Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANN) against conventional ANN, applied on real time intraday traffic volume forecasting.

^{[20]}Both of the proposed variants have outperformed state-of-art techniques using Genetic algorithms and Particle swarm optimization when tested on UCI public dataset and real dataset of Twitter, making it well suitable for multimedia blog volume forecasting.

^{[21]}Most of the traditional marine traffic volume forecasting studies focus on the variation of the traffic volume of a single port or section in time dimension and less research on traffic correlation of associated ports in shipping networks.

^{[22]}

그런 다음 판매량 예측 및 판매자 예측 작업을 안내하는 다중 작업 예측 아키텍처를 소개합니다.

^{[1]}정확한 단기 교통량 예측은 지능형 교통 시스템(ITS)의 교통 관리에서 점점 더 중요해지는 구성 요소가 되었습니다.

^{[2]}우리는 암호 화폐 다중 시장에서 일중 단기 볼륨 예측의 문제를 연구합니다.

^{[3]}독창성/가치 기존의 회색예측모형은 계절변동에 따른 계열예측문제를 풀 수 없었고 분기별 철도여객수요예측에 대한 연구연구가 거의 없었기 때문에 GM(1,1)모형을 추세방정식으로 삼고 계절변동과 결합하였다. 본 연구에서 정확한 예측 결과를 위한 조합 예측 모델을 구축하기 위한 지표.

^{[4]}본 논문은 관련 관광 흐름 이론과 결합하여 관광 고속도로의 교통량 예측 방법을 혁신적으로 제시합니다.

^{[5]}컨테이너 부피 예측에 대한 이전 연구는 ARIMA, SARIMA 및 회귀와 같은 전통적인 통계 방법론을 기반으로 했습니다.

^{[6]}nan

^{[7]}nan

^{[8]}nan

^{[9]}nan

^{[10]}nan

^{[11]}nan

^{[12]}nan

^{[13]}nan

^{[14]}nan

^{[15]}nan

^{[16]}nan

^{[17]}nan

^{[18]}nan

^{[19]}nan

^{[20]}nan

^{[21]}nan

^{[22]}

## Traffic Volume Forecasting 교통량 예측

Accurate short-term traffic volume forecasting has become a component with growing importance in traffic management in intelligent transportation systems (ITS).^{[1]}Combining with the relevant theories of tourism flow, this paper innovatively puts forward the traffic volume forecasting method of tourism expressway.

^{[2]}Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy.

^{[3]}A comparison of results revealed that the SSA–ANN hybrid model could improve the forecasting accuracy of the conventional ANN model in the case of daily traffic volume forecasting.

^{[4]}Short-term traffic volume forecasting is one of the most essential elements in Intelligent Transportation System (ITS) by providing prediction of traffic condition for traffic management and control applications.

^{[5]}The purpose of this study is to develop a model for traffic volume forecasting of the road network in Anamorava Region.

^{[6]}First, we transform the regression problem of traffic volume forecasting into a binary classification problem.

^{[7]}The paper presents the results of qualitative research carried out in the aspect of traffic volume forecasting on selected national roads, supported by a scientific search and discourse on logistic aspects of traffic management, with particular emphasis on Intelligent Transport Systems, in order to verify the effectiveness of the implementation of neural networks.

^{[8]}The present paper provides a comparative evaluation of hybrid Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANN) against conventional ANN, applied on real time intraday traffic volume forecasting.

^{[9]}Most of the traditional marine traffic volume forecasting studies focus on the variation of the traffic volume of a single port or section in time dimension and less research on traffic correlation of associated ports in shipping networks.

^{[10]}

정확한 단기 교통량 예측은 지능형 교통 시스템(ITS)의 교통 관리에서 점점 더 중요해지는 구성 요소가 되었습니다.

^{[1]}본 논문은 관련 관광 흐름 이론과 결합하여 관광 고속도로의 교통량 예측 방법을 혁신적으로 제시합니다.

^{[2]}nan

^{[3]}nan

^{[4]}nan

^{[5]}nan

^{[6]}nan

^{[7]}nan

^{[8]}nan

^{[9]}nan

^{[10]}

## Freight Volume Forecasting

Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume.^{[1]}This paper proposes an effective freight volume forecasting approach for river-sea direct transport without direct historical data.

^{[2]}This paper develops a multi-step approach framework for freight volume forecasting of RSDT in the case that direct historical data are not available.

^{[3]}

## volume forecasting method

Combining with the relevant theories of tourism flow, this paper innovatively puts forward the traffic volume forecasting method of tourism expressway.^{[1]}Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume.

^{[2]}

본 논문은 관련 관광 흐름 이론과 결합하여 관광 고속도로의 교통량 예측 방법을 혁신적으로 제시합니다.

^{[1]}nan

^{[2]}