Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting

2019 ◽  
Vol 119 (3) ◽  
pp. 561-577 ◽  
Author(s):  
Chung-Han Ho ◽  
Ping-Teng Chang ◽  
Kuo-Chen Hung ◽  
Kuo-Ping Lin
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Series Data ◽  
Swarm Optimization ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Seasonal Time Series ◽  
Air Pollutions ◽  
Daily Concentration

PurposeThe purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).Design/methodology/approachThe prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.FindingsSeasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.Originality/valueThis study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network

2016 ◽  
Vol 7 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Rajashree Dash ◽  
Pradipta Kishore Dash
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Financial Time Series ◽  
Random Search ◽  
Particle Swarm ◽  
Series Data ◽  
Swarm Optimization ◽  
Financial Time

In this paper a predictor model using Legendre Neural Network is proposed for one day ahead prediction of financial time series data. The Legendre Neural Network (LENN) is a single layer structure that possess faster convergence rate and reduced computational complexity by increasing the dimensionality of the input pattern with a set of linearly independent nonlinear functions. The parameters of the LENN model are estimated using a Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The HMRPSO is a variant of PSO that uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. Training LENN using HMRPSO has also been compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of LENN for predicting the Bombay Stock Exchange and S&P 500 data sets.

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

An improved morphological weighted dynamic similarity measurement algorithm for time series data

2018 ◽  
Vol 11 (4) ◽  
pp. 486-495
Author(s):  
Ke Yi Zhou ◽  
Shaolin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Morphological Changes ◽  
Series Data ◽  
Similarity Measurement ◽  
Improved Method ◽  
Control Measurement ◽  
Content Type ◽  
Basic Work ◽  
Measurement Algorithm

Purpose The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate. Design/methodology/approach Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system. Findings The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement. Practical implications This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis. Originality/value In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

2018 ◽  
Vol 14 (01) ◽  
pp. 91-111 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
S. R. Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Market Price ◽  
Intuitionistic Fuzzy Set ◽  
Score Function ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

Impact assessment of political administrations on the performance of the construction sector: a time series analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najimu Saka ◽  
Abdullahi Babatunde Saka ◽  
Opeoluwa Akinradewo ◽  
Clinton O. Aigbavboa
Keyword(s):  
Time Series ◽  
Growth And Development ◽  
Time Series Data ◽  
Critical Factor ◽  
Sustainable Growth ◽  
Analysis Of Covariance ◽  
Series Data ◽  
Construction Sector ◽  
Content Type ◽  
Ancova Model

Purpose The complex interaction of politics and the economy is a critical factor for the sustainable growth and development of the construction sector (CNS). This study aims to investigate the effects of type of political administration including democracy and military on the performance of CNS using the Nigerian Construction Sector (NCS) as a case study. Design/methodology/approach A 48 year (1970–2017) time series data (TSD) on the NCS and the gross domestic product (GDP) based on 2010 constant USD were extracted from the United Nations Statistical Department database. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models were used to analyze the TSD. The ANCOVA model includes the GDP as correlational variable or covariate. Findings The estimates of the ANOVA model indicate that democratic administration is significantly better than military administration in construction performance. However, the ANCOVA model indicates that the GDP is more important than political administration in the performance of the CNS. The study recommends for a new national construction policy, favourable fiscal and monetary policy, local content development policy and construction credit guaranty scheme for the rapid growth and development of the NCS. Originality/value Hitherto, little is known about the influence of political administration on the performance of the CNS. This study provides empirical evidence from a developing economy perspective. It presents the relationships and highlights recommendations for driving growth in the construction industry.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

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