scholarly journals Predicting the Albanian economic development using multivariate Markov chain model

2020 ◽  
Author(s):  
Eralda Gjika (Dhamo) ◽  
Lule Basha ◽  
Xhensilda Allka ◽  
Aurora Ferrja
Keyword(s):  
Economic Development ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Consumer Price Index ◽  
Forecast Model ◽  
Chain Model ◽  
Short Term ◽  
Probability Forecast ◽  
Fifth Order ◽  
Yearly Data

In this work, the economic development and relation to social and demography indices in Albania were studied. Four time series (yearly data for the period 1995–2018) were considered: consumer price index (CPI), unemployment rate, inflation and life expectancy. In our approach, a first and fifth order multivariate Markov chain model was proposed to predict the economic situation in Albania in the proceedings years. Tests and accuracy analysis of the model were performed. The prediction probabilities fall in the interval of 0.47 to 0.52 and the accuracy of both models is 75%. Our approach is a short term probability forecast model that can be used by the policymakers to evaluate and undertake initiatives to improve the situation in the country.

Short-term droughts forecast using Markov chain model in Victoria, Australia

2016 ◽  
Vol 129 (1-2) ◽  
pp. 445-457 ◽  
Author(s):  
Siti Nazahiyah Rahmat ◽  
Niranjali Jayasuriya ◽  
Muhammed A. Bhuiyan
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Chain Model ◽  
Short Term

A Deep Learning Based Approach to Predict Sequential Design Decisions

2019 ◽  
Author(s):  
Molla Hafizur Rahman ◽  
Charles Xie ◽  
Zhenghui Sha
Keyword(s):  
Markov Chain ◽  
System Design ◽  
Design Process ◽  
Design Space ◽  
Markov Chain Model ◽  
Sequential Design ◽  
Chain Model ◽  
Short Term ◽  
Design Decisions ◽  
Term Memory

Abstract During a design process, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are important to the development of new algorithms embedded with human intelligence to augment computational design. In this paper, we develop a deep learning based approach to model and predict designers’ sequential decisions in a system design context. The core of this approach is an integration of the function-behavior-structure model for design process characterization and the long short term memory unit model for deep leaning. This approach is demonstrated in a solar energy system design case study, and its prediction accuracy is evaluated benchmarked on several commonly used models for sequential design decisions, such as Markov Chain model, Hidden Markov Chain model, and random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to reply on both short-term and long-term memory of past design decisions in guiding their decision making in future design process. Our approach is general to be applied in many other design contexts as long as the sequential design action data is available.

Application of Improved GM(1,1)-Markov Chain Model of Business Order Prediction

2011 ◽  
Vol 143-144 ◽  
pp. 468-472
Author(s):  
Yi Liu ◽  
Wei Guo Lin ◽  
Ming Zhong Yang
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Business Environment ◽  
Small Sample ◽  
Prediction Methods ◽  
Chain Model ◽  
Order Quantity ◽  
Short Term ◽  
Real World Data ◽  
Strong Ability

Accurate prediction of the order quantity for the next period is very important for the enterprise to enhance the commercial competitive advantage in a highly competitive business environment. GM(1,1) theory is one of the prediction methods that can be built with a small sample and yet has a strong ability to make short-term predictions. The objective of the paper is to propose a order quantity prediction model which is combined the improved GM(1,1) model and Markov chain model .The effectiveness of the proposed approach to the order prediction is demonstrated using real-world data from a famous company in Liuzhou.The results indicate that the method of prediction is satisfying.

scholarly journals Spatio-temporal Markov chain model for very-short-term wind power forecasting

2019 ◽  
Vol 2019 (18) ◽  
pp. 5018-5022 ◽  
Author(s):  
Yongning Zhao ◽  
Lin Ye ◽  
Zheng Wang ◽  
Linlin Wu ◽  
Bingxu Zhai ◽  
...  
Keyword(s):  
Markov Chain ◽  
Wind Power ◽  
Markov Chain Model ◽  
Chain Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Spatio Temporal ◽  
Power Forecasting

scholarly journals APPLICATION OF MARKOV CHAINS IN HUMAN RESOURCES MANAGEMENT IN OSUN STATE PUBLIC SECTOR

2020 ◽  
Vol 5 (1) ◽  
pp. 89-99
Author(s):  
Samson Adebolu Adegbite ◽  
Keyword(s):  
Markov Chain ◽  
Human Resources ◽  
Markov Chain Model ◽  
Human Resources Management ◽  
Forecast Model ◽  
Chain Model ◽  
Tertiary Institution ◽  
Labour Movement ◽  
Resources Management ◽  
Gambler’S Ruin

The purpose of this paper is to evaluate labour forecast and planning based on Markov chain model. The paper made use of improved Gray Markov model and collected data from information collected from a tertiary institution in Osun State, Nigeria. Markov chain was applied to predict the staff requirement between 2016 and 2019. The result revealed that Markov chain can be used to predict and forecast labour requirements which are very useful in labour planning. The study recommend the use of Markov chain model because it is able to forecast adequately the labour requirements and labour movement upward and downward the ladder of an organization. Keywords: Markov chain, planning, forecast, model, gambler’s ruin, human resource, management, strategic.

Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks

2021 ◽  
pp. 1-46
Author(s):  
Molla Hafizur Rahman ◽  
Charles Xie ◽  
Zhenghui Sha
Keyword(s):  
Markov Chain ◽  
System Design ◽  
Design Process ◽  
Markov Chain Model ◽  
Sequential Design ◽  
Chain Model ◽  
Generation Model ◽  
Short Term ◽  
Design Decisions ◽  
Term Memory

Abstract For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems design context. The core of this approach is an integration of the function-behavior-structure model for design process characterization and the long short-term memory unit model for deep leaning. This approach is demonstrated in two case studies on solar energy system design, and its prediction accuracy is evaluated benchmarking on several commonly used models for sequential design decisions, such as the Markov Chain model, the Hidden Markov Chain model, and the random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to rely on both short-term and long-term memory of past design decisions in guiding their future decision making in the design process. Our approach can support human-computer interactions in design and is general to be applied in other design contexts as long as the sequential data of design actions are available.

Short-Term Prediction of Wind Power Output Based on Markov Chain

2013 ◽  
Vol 448-453 ◽  
pp. 1789-1795
Author(s):  
De Xin Li ◽  
Xiang Yu Lv ◽  
Zhi Hui Song
Keyword(s):  
Markov Chain ◽  
Wind Power ◽  
Power Output ◽  
Wind Farm ◽  
Markov Chain Model ◽  
Forecast Accuracy ◽  
Chain Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Wind power short-term predicting technology has a great significance in process of wind power decision-making. Recent years, the technology had been studied extensively in industry. Markov chain model has strong adaptability, forecast accuracy higher and other else advantages, which is suitable for wind power short-term prediction. This paper have set up one step Markov prediction model and based on which predicting short-term wind power output, and taken the historical power data of an actual wind farm in Jilin Province as an example to simulate and analyze. The paper also have proposed and used RMSE, MXPE, MAPE error analysis indicators to analyze simulation results of different status spaces. The results showed that when the status space is 60 the prediction accuracy of the method is best.

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