Proper Forecasting Research Articles

Jaringan Saraf Tiruan untuk Memprediksi Jumlah Pertumbuhan Penduduk di Dinas Kependudukan dan Pencatatan Sipil Kabupate Sumba Barat Daya

The purpose of this research is to create a JST (artificial neural network) model that can forecast population growth at the Population and Civil Registration Office of West Sumba Regency. population growth at the Population and Civil Registration Office of West Sumba Regency. Regency. Regional development planning must consider the increasing number of population, therefore proper forecasting is essential to encourage sustainable policies and initiatives. sustainable policies and initiatives. Because it can identify complex patterns in past data and produce more accurate forecasts than traditional techniques, an ANN model is used. traditional techniques, the ANN model is used. The data used in this study is the population growth of Southwest Sumba Regency over the past including characteristics such as birth and death rates and population movements. deaths and population movements. The backpropagation algorithm is used to optimize the multilayer perceptron (MLP) architecture for ANN training. Separating the data into training and testing sets and assessing the models model using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) based on the error. Error (RMSE) based on the prediction error are the steps involved in the training process. involved in the training process. The research findings show that, with a low level of error, the artificial neural network model can estimate the population increase in Southwest Sumba Regency with a reasonable level of accuracy. reasonable level of accuracy. The model is expected to serve as a reference for relevant authorities to better manage population data and as a tool to create more focused and successful population policies.

MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

The impact of talent management on human resources efficiency

The belief that talented people create prosperous organizations contributed to the accomplishment of talent management concepts into reality. Highly capable professionals or talented employees who systematically nurtured the best skills typically demonstrate high performance and serve as one of the key success factors for the organizations. Organizations face challenges attracting and maintaining talented employees more than assets. Researches demonstrate that one of the major challenges an organization experience is the aspect of talent management. The objective of the present study is to examine how talent management functions impact the efficiency of human resources. We conducted a sample survey in the metropolitan branch company of the major shareholding company. SPSS 23.0 and Smart PLS 3.0 are used in data analysis and we conducted several tests like reliability tests, correlation analyses, and structural equation models (SEM) to produce the results. The survey results showed that the practice of talent management improves the efficiency of human resources. Moreover, it proved that proper forecasting of talent management needs that foresee opportunity career development and nurture the work culture which would support the mission and vision of the organization makes it possible to increase the efficiency of the human resources.

Comparison of SES and SMA Method Against Production Level Property of Fabrication Precision Engineering and Its Effect on Production Planning (Case Study PT X)

One of the goals of forecasting is to be able to predict the data needed in the future, one of which is the data on demand for the amount of production in a company. PT X is a manufacturing company based on demand or custom. This leads to uncertainty in the use of required materials, so proper forecasting is needed to estimate the material stock requirements. This study used single exponential smoothing and single moving average methods with quantitative approaches to aluminum materials. By calculating forecasting using these two methods, it is possible to find the best method for use by PT X. Based on the test, the SES method has the smallest error rate so it can be used to analyze the data, with α=0.8 yielding a forecast in the 13th month of 308.71408 pcs.

The modeling of earnings per share of Polish companies for the post-financial crisis period using random walk and ARIMA models

The proper forecasting of listed companies’ earnings is crucial for their appropriate pricing. This paper compares forecast errors of different univariate time-series models applied for the earnings per share (EPS) data for Polish companies from the period between the last financial crisis of 2008–2009 and the pandemic shock of 2020. The best model is the seasonal random walk (SRW) model across all quarters, which describes quite well the behavior of the Polish market compared to other analyzed models. Contrary to the findings regarding the US market, this time-series behavior is well described by the naive seasonal random walk model, whereas in the US the most adequate models are of a more sophisticated ARIMA type. Therefore, the paper demonstrates that conclusions drawn for the US might not hold for emerging economies because of the much simpler behavior of these markets that results in the absence of autoregressive and moving average parts.

The report of the 7th National Scientific Conference Poland's national security. Threats and determinants of changes. Challenges for state security in the era of 21st century threats,Gorzw Wielkopolski, April 21 22, 2022

On April 21-22, 2022, at the Academy of Jakub from Parady in Gorzw Wielkopolski, the seventh edition of the nationwide scientific conference was entitled Poland's national security. Threats and determinants of change. This year's theme were challenges for state security in the era of 21st century threats. Contemporary challenges and threats in the field of security require scientific reflection, exchange of views, construction of improved concepts of action and proper forecasting and anticipation of changes taking place in the security environment, which should help rationalize and improve the effectiveness of actions taken by the state.

Spatial optimization of the economic benefits of the potential hydropower locations: study case upper citarum river

Hydropower is a renewable energy source that provides clean energy services and contributes to climate change mitigation by replacing fossil fuel energy production sources. The Upper Citarum Watershed in West Java, Indonesia, is described as a giant basin known as the Bandung Basin, which is type by mountainous relief with an elevation of 750-2300 meters above sea level, a slope of 30-90%, and a radiating river flow pattern. This study focuses on the potential of small hydropower with a run-of-river scheme that acts as a filler for the study of hydropower potential with a pump storage scheme by the Australian National University. Climate data in the study area were analyzed using the SWAT model to obtain Cumulative Discharge Frequency and identify potential discharge by adopting the method of previous studies. The results identified 7 SHPs with P exceeding 500 kW and below 2000 kW. BHUMI ATR BPN can predict land value and land cover in areas designated for hydropower development. The findings suggest improvements in aspects such as proper forecasting, on-the-ground valuation, and calculations relating to economically viable sustainability.

Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

Financial crisis prediction (FCP) received significant attention in the financial sector for decision-making. Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy. Conventionally, numerous approaches have been developed in the design of accurate FCP processes. At the same time, classifier efficacy and predictive accuracy are inadequate for real-time applications. In addition, several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets. Thus, there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets. This paper presents a novel multi-vs. optimization (MVO) based feature selection (FS) with an optimal variational auto encoder (OVAE) model for FCP. The proposed multi-vs. optimization based feature selection with optimal variational auto encoder (MVOFS-OVAE) model mainly aims to accomplish forecasting the financial crisis. For achieving this, the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization. In addition, the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach. Followed by, the variational auto encoder (VAE) model is applied for the categorization of financial data into financial crisis or non-financial crisis. Finally, the differential evolution (DE) algorithm is utilized for the parameter tuning of the VAE model. A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market

In order to enhance the operational cost and efficient management of all the power system equipment in a micro grid requires proper forecasting of energy and scheduled power dispatch. Due to the uncertainties in the load demand and the interconnection of intermittent source of energy, the operational cost becomes high. The traditional grid also requires accurate prediction of per unit price in the electricity trading market. Electricity price forecasting plays a vital role. Time series based machine learning algorithm are generally used to calculate unit price in lieu of power loss in a smart grid architecture. However, while dealing with large data set, generated in every 15 s, it is very challenging and time consuming and at the same time large data set may create curve over fittings. In this paper, a novel approach has been made by combining both flower pollination algorithm and machine learning for forecasting the unit price. The proposed model comprises of three basic models such as feature selection, principal component analysis and novel hybrid model for optimization and regression. Three different sigma value such as 0.8,0.9 & 0.94 with Gaussian surface has been used to test the algorithm Finally, the algorithm has been tested with IoT architecture for robustness evaluation.

МЕХАНІЗМИ ЗМІЦНЕННЯ ЕКОНОМІЧНОЇ БЕЗПЕКИ ГРОМАДЯН УКРАЇНИ В УМОВАХ СУСПІЛЬНО-ПОЛІТИЧНОЇ КРИЗИ ТА ЗОВНІШНІХ ВИКЛИКІВ

The article outlines systemic threats, substantiates the need and highlights mechanisms for strengthening the economic security of the citizens of Ukraine in the context of exacerbation of socio-political crisis and external challenges. The existing in inconsistency of basic social standards in Ukraine and their institutional provision with real conditions and needs of citizens causes inadequate state guarantee of its beneficiaries (citizens) rights to life, earnings, housing, health care and, in general, - to self-reproduction and development. Given the aggravation of socio-political and socio-economic crises, deepened by hostilities in eastern Ukraine, the annexation of the Autonomous Republic of Crimea, ensuring economic security of citizens, especially vulnerable groups - migrants, large and young families, the elderly is highly important. The main reason for the high level of vulnerability of socio-economic rights of citizens is the imperfection of state and regional policy, inefficient management of state resources, which leads to their privatization or hidden unfair redistribution of shadow schemes in favor of political elites. As a result, this fact has led to excessive stratification of the population in terms of income, when about 80% of the total population of Ukraine receives a monthly income below the real subsistence level, the spread of poverty among workers. The analysis shows that the reforms in Ukraine are carried out without proper forecasting and assessment of the expected effectiveness, taking into account the key performance indicators (KRI) to ensure the economic security of the main beneficiary of the state - a citizen of Ukraine. Further reform of the socio-economic sphere requires the introduction of institutional changes with the obligatory priority of economic security of the citizens.

Impact of Harmonic Currents of Nonlinear Loads on Power Quality of a Low Voltage Network–Review and Case Study

The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor (THDI) and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities’ nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor (THDI). The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear loads should be a key factor in harmonic currents’ pollution study. Proper forecasting of the level of harmonic currents injected into the utility grid helps to maintain the quality of electricity at an appropriate level and reduce active power losses, which have a direct impact on the price of electricity generation.

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton’s laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent’s position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon’s rank-sum tests.

Material Cost Optimization and Quality Control Improvement of Building Construction

A building construction project cannot be completed successfully by only planning and scheduling. Project controlling that consists of project monitoring and updating, is the other critical stage. Construction materials cost and quality are becoming a severe problem in Ethiopia, especially in Ambo Town, where little consideration is given. The study tried to evaluate different factors affecting the materials cost and quality on building construction site, rank significant factors that are prevalently affecting the cost and quality of materials used for building projects from the point of view of the respondents, and established a model to optimize materials cost and materials quality using multilinear regression method to come up with the model best fit. Purposive sampling was used, and 180 questionnaires were administered on building construction firms to analyze Material cost Optimization and quality control Improvement- Case Study of Ambo Town. To achieve this, a questionnaire survey containing factors affecting Building construction material cost and quality, which were identified by literature and Interview, was analyzed by using the Relative Importance Index, Pearson's correlation, and Multilinear regression model. Variables are statistically significant at 95% confidence level is assessed that all of the independent variables are positively related to Building Construction material cost and quality. The RII results showed that Material Wastage minimization (RII = 0.908), Proper workmanship (RII= 0.859), Sufficient Training of workers/staff (RII = 0.876), Proper forecasting of materials price in the market (RII = 0.850), Using suitable, safe and secure storage (RII = 0.886), Quality checking for each delivery of the aggregates to the site (RII = 0.889), Cutting of materials ( glass, tiles, plywood) (RII = 0.870) and Design changes (RII = 0.770) were ranked as the most significant among factors affecting building construction materials. Also, Two Multiple linear regression models were established for both dependent variables (material cost and material quality). The analysis results showed all independent variables were significant and had a strong positive relationship with dependent variables. The values of R2 for the developed equations indicated 0.771 for material cost and 0.777 for material quality. Hence, the basic recommendations such as planning and monitoring construction activities and inspection and verification of materials were anticipated to be exercised by key role players of the construction industry.

The impact of modified rate of precipitation conversion parameter in the convective parameterization scheme of operational weather forecast model (GFS T1534) over Indian summer monsoon region

The performance of the present operational global forecast system (GFS) at T1534 (~12.5 km) horizontal resolution with modified fractional cloud condensate to precipitation conversion parameter in the revised simplified Arakawa-Schubert (RSAS) convection scheme is evaluated for the summer monsoon seasons of 2018 and 2019 over the Indian region. The modified parameter has the form of an exponentially decreasing function of temperature above the freezing level. In contrast, below the freezing level, it is constant and is similar to default conversion parameter. The results reveal that the GFS T1534 with modified conversion parameter (EXPT) shows better fidelity in forecasting the mean summer monsoon rainfall over the Indian subcontinent region as compared to default GFS T1534 (CTRL). The rainfall probability distribution function analysis indicates a notable improvement in forecasting moderate and heavier category rainfall in EXPT as compared to CTRL. The improved distribution of total rainfall is found to be contributed by the proper forecasting of convective and large-scale rainfall in EXPT. It is likely that the reduced rate of conversion of cloud condensate to convective precipitation above the freezing level leads to a reduction in the convective rainfall, which eventually increases the moisture in the upper level through detrainment and hence enhances the large-scale precipitation. Further, EXPT shows relative improvement in forecasting outgoing longwave radiation, wind circulation, cloud fraction, dynamical-thermodynamical processes, and moist-convective feedback through improved lower tropospheric moistening over the Indian region. Finally, various skill score analyses suggest that EXPT shows better skill in predicting moderate and heavier category rainfall with longer lead time over the Indian subcontinent region. Considering the large socioeconomic impact of heavy and extreme precipitation over India, the modified conversion parameter can be incorporated in the present operational GFS T1534 model.

Evaluation of Asphalt Concrete Airport Pavement Conditions Based on the Airfield Pavement Condition Index (APCI) in Scope of Flight Safety

Airoport infrastructure development requires care to maintain it in proper technical condition. Due to this, airport pavements should be constantly monitored, and, above all, correctly managed. High-level airport pavement management requires access to reliable information about their current technical condition as well as proper forecasting of this condition in the future. Obtaining good quality information about the technical condition of airport pavement should be based on a proven methodology, taking into account the introduced quality management system. The authors propose a method of technical pavement condition assessment based on the Airfield Pavement Condition Index (APCI), taking into account not only the results of the surface deterioration inventory, but also repair overviews, load bearing capacity, evenness and roughness of the surface, as well as the surface tensile bond strength. The method was developed during long-term work financed by the Ministry of Science and Higher Education. At the beginning of the article, the authors focus on reviewing the currently available methods of assessing the technical condition of the pavement. Then they briefly present the most popular surface assessment method based on the PCI indicator. Afterwards, a proprietary asphalt pavement assessment method based on the APCI indicator is proposed and an example of how to use the method is presented. Finally, they discuss the results and summarize the work done, and present further directions of work.

Comparison of Holt and Brown's Double Exponential Smoothing Methods in The Forecast of Moving Price for Mutual Funds

Mutual funds are one of the promising investment media where the risk is directly proportional to the size of investment growth. With proper forecasting of NAV price movements will greatly help investors to make purchases and sales transactions, therefore the authors offer the use of two different forecasting methods namely Brown's method and Holt method in double exponential smoothing to get predictions of NAV price movements. The effectiveness of the use of the method will be measured from the value of Mean Average Percentage Error (MAPE). From the calculation results obtained by the data that the Holt method produces forecasting for 1809,657 with the best α value of 0.6 and MAPE of 0.644373568, while for the Holt method obtained forecasting value of 1810,924 with the α value and the best β value of 0.9 and 0.1 and the smaller MAPE value of 0.61604262 . Looking at the amount of MAPE generated, the Holt method has a smaller forecasting error rate when compared to Brown’s method.

Snow cover phenology is the main driver of foraging habitat selection for a high-alpine passerine during breeding: implications for species persistence in the face of climate change

High-alpine ecosystems are strongly seasonal and adverse environments. In these ecosystems, the brevity of optimal breeding conditions means species must efficiently track spatiotemporal variations in resources in order to synchronise their reproductive effort with peaks in food availability. Understanding the details of prey-habitat associations and their seasonality in such ecosystems is thus key for deciphering species’ ecological niches and developing sound conservation action. However, the ecological requirements of high-alpine avifauna remain poorly documented. Furthermore, mountain ranges in the Old World are affected not only by profound alterations of climate, but also by changes in land-use, the interaction of which hampers both proper forecasting of species’ resilience to environmental change and delivery of evidence-based conservation guidance. Here, we investigate the prey-habitat associations of a high-alpine passerine, the White-winged Snowfinch (Montifringilla nivalis), by radio-tracking breeding adults in the Swiss Alps. In late spring and early summer, Snowfinches foraged preferentially next to invertebrate-rich, melting snow patches where Tipulidae larvae abound. Later, in mid-summer, they favoured flower-rich alpine meadows. When foraging, they always preferred short ground vegetation while avoiding rock and scree. Their pattern of foraging habitat selection reflects trade-offs between food abundance and accessibility, i.e. prey availability. The reliance of this passerine on a habitat mosaic where snow plays a major role questions its ability to cope with climate change due to future habitat loss and potential phenological mismatches. Targeted grazing could possibly help in habitat management by aiming at maintaining invertebrate-rich meadows with short vegetation. Yet, it remains an open question whether habitat management would suffice to compensate for the potentially detrimental effects of the progressive retreat of snow fields to higher elevations.

Model of two-phase production from gas wells conning water inspired by natural processes

Water coning is one of the most challenging problem in the petroleum industry, because it can affect the well productivity and increase production costs. The proper forecasting of the production profile is very important, but it is difficult to find the exact relationship between produced gas and water. In this study, we propose a novel mathematical model for the two-phase production forecasting of gas wells conning water. The developed model is based on the two species Lotka–Volterra model and includes a system of two ordinary differential equations. The proposed model uses the analogies of the two-phase flow in the reservoir to the natural competition processes. The developed model was applied to some real industrial examples. The results show that the model accurately trends the historical data and ensures reliable production forecast. Hence, the proposed model can be a useful tool to predict future performance of gas wells coning water.

Taxation and revenues of central places as the regulator of available human resources in manufacturing - related to the bid rent curve

Ageing and shrinking of the European population will decrease the availability of human resources in manufacturing. Therefore, the wage policy will operate in an increasingly sensitive environment, which will need to be well understood and able to fast respond to its changes. To understand and forecast the attractiveness of manufacture companies and abilities to attract additional workers, it is not enough to study wage policy independently, but also to know the real estate market and the distance of areas from where we can attract new workers. It means, therefore, wage policy should be tied to the location, real estate market and fiscal policy of a city in which the company and the real estate market are in order to achieve the optimum effect in finding new employees. In the paper, we show how useful it is to analyse the bid rent curve of the housing market at the central places through the changes of parameters in a gravity model. In such a model, the municipalities are considered as the regional centres, where different regional centres compete for new workers. Namely, the municipalities represent essential cells, competing with the other municipalities in the total economic area. A successful fiscal policy leads regions, and local communities along the path of balanced and environmentally friendly long-term growth or a steady decline analysed through the bid rent curves, but also influence the availability of human resources. The paper discusses the impact of changes in the wages, property taxes and municipal revenues on the bid rent curve and attractiveness for migrants. Therefore, the indicators evaluated through the changes of parameters in a gravity model, which are subject of the changed wages and taxation policy, gives proper forecasting of the future dynamics. From all these interactions the inflow of new workers can be forecasted, and the appropriate wage policy can be chosen as the regulator of available human resources.

Disaster management in Bangladesh: developing an effective emergency supply chain network

This study has addressed and identified the problems in managing the existing emergency supply chain of Bangladesh in all phases of operation in terms of the primary drivers of the supply chain. It has also attempted to conceptualize and suggest an effective emergency supply chain. In this context, a thorough field investigation in several districts was conducted among the employees of the organizations sharing common information with similar protocols and implications (interoperable). Information was collected from the employees of all the participating organizations involved in disaster management through a semi-structured questionnaire based survey. The respondents addressed and illustrated several interconnected reasons which are inhibiting proper forecasting, procurement, storage, identification of affected people, and distribution. The respondents pointed out that the mismatching of objectives in the different organizations resulted in non-interoperability among the participating organizations. These issues are related to the malfunctioning of management with multidimensional organizational conflicts. Reflecting those issues, an emergency supply chain for disaster management is proposed in this study.