Dynamic Forecasting Algorithm of Inbound Ice and Snow Tourism in China Based on Improved Deep Confidence Network

Ice and snow-based tourism is getting popular around the world and it is one of the major sources of revenue for a region with required facilities. According to a report by China Daily, China was expected to witness 230 million tourist visits in 2020-2021 with a total revenue generation surpassing 390 billion yuan. In order to promote the ice and snow tourism, proper arrangements such as accommodation, transport facility, and energy provision for heating and food need to be arranged as per the demand of the visitors at a certain period of time. A tourist prediction system can help in this regard for good estimation but considering the problems of traditional ice and snow tourism systems, specifically the prediction accuracy and long forecasting time, a dynamic forecasting algorithm for ice and snow inbound tourism based on an improved deep confidence network is proposed. The system analyzes the relationship between the demand for ice and snow inbound tourism and the level of national economic development, people’s living standards, demographic characteristics, traffic conditions, and tourism supply capacity. It then takes the influencing factors of ice and snow inbound tourism demand as sample data and arranges the sample data sequence. The inbound tourism demand dynamic prediction model uses an improved deep confidence network to learn and train the prediction model, input test data into the trained model, and output the dynamic prediction value of ice and snow inbound tourism demand in the output layer to obtain the prediction result. The simulation results show that the proposed algorithm has improved accuracy in predicting the demand of inbound tourism for ice and snow, whereas the forecasting time is reduced.

Tourism and Big Data: Forecasting with Hierarchical and Sequential Cluster Analysis

A new Big Data cluster method was developed to forecast the hotel accommodation market. The simulation and training of time series data are from January 2008 to December 2019 for the Spanish case. Applying the Hierarchical and Sequential Clustering Analysis method represents an improvement in forecasting modelling of the Big Data literature. The model is presented to obtain better explanatory and forecasting capacity than models used by Google data sources. Furthermore, the model allows knowledge of the tourists’ search on the internet profiles before their hotel reservation. With the information obtained, stakeholders can make decisions efficiently. The Matrix U1 Theil was used to establish a dynamic forecasting comparison.

Mathematical Approach for System Repair Rate Analysis Used in Maintenance Decision Making

Reliability, the number of spare parts and repair time have a great impact on system availability. In this paper, we observed a repairable system comprised of several components. The aim was to determine the repair rate by emphasizing its stochastic nature. A model for the statistical analysis of the component repair rate in function of the desired level of availability is presented. Furthermore, based on the presented model, the approach for the calculation of probability density functions of maximal and minimal repair times for a system comprised of observed components was developed as an important measure that unambiguously defines the total annual repair time. The obtained generalized analytical expressions that can be used to predict the total repair time for an observed entity are the main contributions of the manuscript. The outputs of the model can be useful for making decisions in which time interval repair or replacement should be done to maintain the system and component availability. In addition to planning maintenance activities, the presented models could be used for service capacity planning and the dynamic forecasting of system characteristics.

Dynamic Forecasting of Government Foreign Debt: Case of Indonesia

This study aims to forecast the value of the Indonesian government foreign debt in 2020-2024. The secondary data of time series during the period of 2010-2019 on Indonesian government foreign debt are used as the basis of forecasting for the next five years by using ARIMA (Autogressive Integrated Moving Average). The results show that the selected ARIMA models for forecasting are ARIMA (3,1,3) after the unit root test is carried out and 16 ARIMA models are tested. The value of government foreign debt is predicted to keep increasing from 2020 to 2024 amounted to USD 253.01. Then, compared to government debt in January 2010, within 11 years, government foreign debt is predicted to rise by 169.6%.

A Dynamic Forecasting Model for Scientific Migration in the Region

The population migration has attracted attention for more than a decade. As migration consequences differ in terms of characteristics and directions, governments worldwide are looking for solutions to regulate migration flows. The study aims to systematise push-pull factors of migration by analysing existing cases, as well as to build a model for predicting migration considering the quantitative interpretation of such factors. While migration factors are quite similar regardless of the country of residence, their main differences are compatibility and hierarchy. The most frequently mentioned factors include the expectation of income increase, improvement in the quality of life, professional aspects. Simultaneously, a certain pattern emerges: if a migrant’s material and economic needs are satisfied in the country of departure, they pay more attention to intangible/non-economic benefits (quality of life, infrastructure, etc.). A dynamic forecasting model for scientific migration has been developed based on the theory of positional games. The model demonstrates the changes in migration flows by describing the behaviour of a rational individual who seeks to maximise benefits from migration. The result of the simulation is a short-term forecast of trends in scientific migration of Ural scholars to key migration countries. The model predicts the intensification of migration flows to the leading Asian countries, their alignment with flows to America, and a decrease in migration to European countries. This forecast is characterised by a direct dependence of the dynamics of scientific migration flows on the socio-economic development of migration destinations. Practical implications of this study include the development of a predictive model describing migration flows in the short term as an analytical tool and systematisation of pull-push factors as key indicators for managing the migration flows of scientists. In addition, the research proposes measures positively affecting the balance of scientific migration.

Dynamic forecasting model for indoor pollutant concentration using recurrent neural network

Accurate and reliable indoor pollutant concentration prediction is essential to solve the time-lag problem of indoor air quality control systems. Thus, the representation of time in pollutant forecasting models is very important. One approach is to introduce an Elman neural network using a direct inference strategy into the time series forecast of indoor pollutant concentration. In this study, measurements of CO2 (ppm), total volatile organic compounds (mg/m3), particulate matter with a diameter smaller than 2.5 µm (PM2.5; µg/m3), the indoor dry bulb temperature (°C) and relative humidity (%) were carried out in a classroom at a middle school in Beijing, China. To identify air pollution antecedents, input selection was conducted based on correlation analysis. The results show that the information provided by the PM2.5 time series can better simulate the dynamic relationship between input and output data ([Formula: see text]= 0.963 and R2 = 0.928). In addition to the overall goodness of fit ([Formula: see text] = 0.982) of the CO2 time series, the peak and valley prediction capability of the model was evaluated using the relative peak error ( RPE) metric. Information from the valleys of the CO2 time series gives good results ([Formula: see text]). Therefore, a dynamic forecasting model with a direct inference strategy is a capable tool for identifying proper air pollution antecedents.