scholarly journals Information technology for time series forecasting by the method of the forecast scheme synthesis

2021 ◽  
Vol 3 (2) ◽  
pp. 81-86
Author(s):  
O. Yu. Mulesa ◽  
◽  
F. E. Geche ◽  
A. Ye. Batyuk ◽  
O. O. Melnyk ◽  
...  
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Linear Combination ◽  
Optimization Problem ◽  
Quality Criterion ◽  
Quality Criteria ◽  
Time Series Forecasting ◽  
Percentage Error ◽  
Forecast Models

The study is devoted to the development of information technology for forecasting based on time series. It has been found that it is important to develop new models and forecasting methods to improve the quality of the forecast. Information technology is based on the evolutionary method of synthesis of the forecast scheme grounded on basic forecast models. The selected method allows you to consider any number of predictive models that may belong to different classes. For a given time series, the weight coefficients with which the models are included in the resulting forecast scheme are calculated by finding the solution to the optimization problem. The method of constructing the objective function for the optimization problem in the form of a linear combination of forecasting results by basic forecasting models is shown. It is proposed to find the solution to the optimization problem using a genetic algorithm. The result of the method is the forecast scheme, which is a linear combination of basic forecast models. To assess the quality of the forecast, it is suggested to use forecasting errors or forecast volatility calculated as the standard deviation. Forecast quality criteria are selected depending on the context of the task. The use of forecast volatility as a quality criterion, with repeated use of technology, will reduce the deviation of forecast values from real data. The structural scheme of information technology is developed. Structurally, information technology consists of two blocks: data processing and interpretation of the obtained values. The result of the application of the developed information technology is the production rules for determining the predicted value of the studied quantity. Experimental verification of the obtained results was performed. The problem of forecasting the number of religious organizations in Ukraine based on statistical data from 1997 to 2000 has been solved. The autoregression method and the linear regression model were chosen as the basic forecast models. Based on the results of using the developed information technology, the weights of the basic models were calculated. It is demonstrated that the obtained forecast scheme allowed to improve the average absolute percentage error and forecast volatility in comparison with the selected models. Keywords: information technology; time series; forecasting; evolutionary technologies; forecast volatility; synthesis of the forecast scheme.

THE QUALITY OF PUBLIC HOSPITALS IN CAN THO CITY, 2016

2018 ◽  
Vol 8 (6) ◽  
pp. 76-81
Author(s):  
Chu Cao Minh ◽  
Thang Vo Van ◽  
Dat Nguyen Tan ◽  
Hung Vo Thanh
Keyword(s):  
Hospital Medicine ◽  
Secondary Data ◽  
Quality Criterion ◽  
Quality Criteria ◽  
Public Hospitals ◽  
Ministry Of Health ◽  
Cross Sectional ◽  
Significant Difference ◽  
Can Tho City

Background: The criteria set of assessing hospital quality in Vietnam in 2016 was revied from the criteria set in 2013 by the Ministry of Health in order to help hospitals to self-assess towards improvinge quality of hospitals in the international integration context. The study aimed to assess the quality of public hospitals in Can Tho City according to the revised criteria set of the Ministry of Health in 2016 and compare the quality among three hospital ranks (including grade I, grade II, and grade III) via to 5 groups of quality criteria. Methods: A cross-sectional study, using secondary data analysis was applied to assess the service quality of 7 general public hospitals in Can Tho City. Results: The average total score of 7 hospitals is 245 and the average for the criteria of 7 hospitals is 2.99, which is just satisfactory. In the criterion of quality, criterion D and E had the lowest scores compared to the other three groups. There was no statistically significant difference (p = 0.076) among the mean scores for the three hospital categories. Conclusion: The quality of public hospitals in Can Tho city in 2016 only reached moderately good level (2.99). Interventions should be developed to improve the quality of hospitals, with particular emphasis on improving the quality of criteria groups D and E. Key words: Quality, hospital, medicine, health, public, Can Tho

scholarly journals Comparative analysis of the forecast for variation of the parameters of the hybrid linguistic model

2019 ◽  
Vol 3 (122) ◽  
pp. 32-41
Author(s):  
Ihor Vsevolodovych Baklan ◽  
Tetiana Viktorivna Shulkevych
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Comparative Analysis ◽  
Empirical Evidence ◽  
Optimal Parameters ◽  
Statistical Accuracy ◽  
Linguistic Model ◽  
Linguistic Approach ◽  
Linguistic Models

Using a hybrid linguistic approach to model numerical images in the form of time series using probabilistic grammars based on hidden time series and implement information technology to build sets of linguistic models and their hybrids that describe the dynamics of selected time series of processes of different nature.In the article the results of computational experiments are considered, the quality of forecasting of time series of diverse nature at various parameters was proved. The goal of the current research is to provide empirical evidence of the suitability of using a hybrid linguistic approach for predicting time series.Experimental way to find the optimal parameters of the algorithm. The algorithm was applied to a variety of time series (social, medical, financial and economic), calculated the statistical accuracy of the forecast. Experiments have shown that the algorithm consistently performs the forecast of values in a range of 3-4 steps forward and forecasts the trend change by 3-5 steps.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

An unbiased GM(1,1)-based new hybrid approach for time series forecasting

2016 ◽  
Vol 6 (3) ◽  
pp. 322-340 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N. Seneviratna ◽  
Wei Jianguo ◽  
Hasitha Indika Arumawadu
Keyword(s):  
Time Series ◽  
Hybrid Approach ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Second Phase ◽  
Suitable Model ◽  
Content Type ◽  
Ex Post ◽  
Hybrid Forecasting

Purpose The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka. Design/methodology/approach The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error. Findings The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models. Practical implications The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future. Originality/value The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.

scholarly journals Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Moh.Hasanudin Marliyati ◽  
Sri Murtini ◽  
Resi Yudhaningsih ◽  
Retno Retno
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Communication Skills ◽  
Time Series Data ◽  
Soft Skills ◽  
Series Data ◽  
Central Java ◽  
Hard Skills ◽  
Main Components

<p>This research aimed at exploring the quality of accounting diploma <br />students during their internship program in industries. The term of student’s <br />quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. <br />Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and <br />analyzed using statistic descriptive. The result showed that average students’ <br />quality during their internship was good. Among the 5 skills observed, the <br />corresponding industries ranked teamwork skills as the highest, followed by <br />independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.</p>

scholarly journals Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)

Computation ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 70
Author(s):  
YM Tang ◽  
Ka-Yin Chau ◽  
Wenqiang Li ◽  
TW Wan
Keyword(s):  
Time Series ◽  
Hong Kong ◽  
Stock Price ◽  
Short Term Memory ◽  
Economic Recession ◽  
Time Series Forecasting ◽  
Share Price ◽  
Percentage Error ◽  
Learning Technology ◽  
Logistics Industry

Time series forecasting technology and related applications for stock price forecasting are gradually receiving attention. These approaches can be a great help in making decisions based on historical information to predict possible future situations. This research aims at establishing forecasting models with deep learning technology for share price prediction in the logistics industry. The historical share price data of five logistics companies in Hong Kong were collected and trained with various time series forecasting algorithms. Based on the Mean Absolute Percentage Error (MAPE) results, we adopted Long Short-Term Memory (LSTM) as the methodology to further predict share price. The proposed LSTM model was trained with different hyperparameters and validated by the Root Mean Square Error (RMSE). In this study, we found various optimal parameters for the proposed LSTM model for six different logistics stocks in Hong Kong, and the best RMSE result was 0.43%. Finally, we can forecast economic recessions through the prediction of the stocks, using the LSTM model.

scholarly journals Quality blues: traditional knowledge used for natural indigo identification in southern China

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yuru Shi ◽  
Libin Zhang ◽  
Lu Wang ◽  
Shan Li ◽  
Zuchuan Qiu ◽  
...  
Keyword(s):  
Particle Size ◽  
Traditional Knowledge ◽  
High Performance ◽  
Southern China ◽  
Quality Criterion ◽  
Quality Criteria ◽  
Local People ◽  
Material Basis ◽  
Ph Range

Abstract Background As one of the oldest traditional dyes, people worldwide have used natural indigo for centuries. Local people have unique knowledge about indigo identification, which is crucial for indigo quality control and determining the dyeing effects. However, such traditional knowledge is rarely documented and explained. Therefore, the aims of this study were to document and assess the traditional knowledge used by local people when identifying natural indigo paste as well as quantitatively explore the characteristics and material basis of such traditional knowledge. Method Three field surveys were conducted between 2019 and 2020. A total of 283 traditional indigo-paste artisans were interviewed in Guizhou, Yunnan, and Fujian Provinces. The frequency of citation, mention index, and fidelity level of each indigo-paste quality criterion were calculated to determine the most commonly used, recognized, and important quality criteria. To explore the characteristics and material basis of the traditional knowledge, we analyzed 21 indigo-paste samples using high-performance liquid chromatography with diode-array detection (HPLC-DAD), pH, and particle size analyses. Results Local people possess unique knowledge to identify natural indigo. Based on this knowledge accumulated over thousands of years, four criteria (color, taste, touch, and dyeing ability) were chosen by local people, and using these criteria, nature indigo was divided into five quality grades. The best quality indigo paste was judged according to the following folk criteria: dark blue in color with a purple-red luster; smooth and difficult to wipe off; having a sweet, bitter or spicy taste; and easy cloth dyeing. Additionally, the higher the contents of indigo and indirubin—especially indirubin—the better is the quality of the indigo paste. Within the pH range of 9–12, high-quality indigo-paste was more acidic. There was no significant relationship between particle size and quality. Conclusion The ancient methods used by local people for identifying natural indigo are comprehensive and unique. By documenting the various folk quality criteria and conducting quantitative analyses, this study revealed the importance of indirubin and pH for assessing the quality of indigo paste. These findings differ from existing quality standards for synthetic indigo. Amid rapid modernization, traditional knowledge remains invaluable as a world heritage of humanity that warrants preservation.

scholarly journals An Empirical Study of Error Evaluation in Trend and Seasonal Time Series Forecasting Based on SSA

2018 ◽  
pp. 945-960
Author(s):  
Winita Sulandari ◽  
Subanar Subanar ◽  
Suhartono Suhartono ◽  
Herni Utami ◽  
Muhammad Hisyam Lee
Keyword(s):  
Time Series ◽  
Numerical Study ◽  
Singular Spectrum Analysis ◽  
Real Data ◽  
Time Series Forecasting ◽  
Percentage Error ◽  
Error Evaluation ◽  
Data Set ◽  
Seasonal Time Series ◽  
Deterministic Function

SSA (Singular Spectrum Analysis) starts to become a popular method in decomposing time series into some separable and interpretable series. This study provides an error evaluation in the SSA-based model for trend and multiple seasonal time series forecasting. This error evaluation is obtained by means of a numerical study on the mean square error of the estimators and mean absolute percentage error of the forecast values. Four distinct types of data generating processes (DGP) with varying sample sizes are considered in this experimental study. The parameters are estimated from the component series of SSA. Each DGP is decomposed into trend, periodic and irregular components. All these components except the irregular one are fitted by appropriate deterministic function separately. Based on the numerical simulation results, the estimated parameters are closer to the true values as the sample size increases. As the illustrative example of the real data set implementation, we used the monthly atmospheric concentrations of CO2 from Moana Loa observatory for period January 1959 to June 1972. The proposed method produces better forecast values than the results of SSA-LRF (Linear Recurrent Formula) and TLSAR (Two Level Seasonal Autoregressive). The results encourage the improvement in the time series modeling on the more complex pattern.

A Study on Time Series Forecasting using Hybridization of Time Series Models and Neural Networks

2020 ◽  
Vol 13 (5) ◽  
pp. 827-832
Author(s):  
Iflah Aijaz ◽  
Parul Agarwal
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Hybrid Models ◽  
Time Series Forecasting ◽  
Percentage Error ◽  
Mean Square

Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting. Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events. Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE). Results: Table 1 summarizes important papers discussed in this paper on the basis of some parameters which explain the efficiency of hybrid models or when the model is used in isolation. Conclusion: The hybrid model has realized accurate results as compared when the models were used in isolation yet some research papers argue that hybrids cannot always outperform individual models.

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