scholarly journals Peramalan Data IHSG Menggunakan Fuzzy Time Series

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Real Time ◽  
Soft Computing ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Time Data ◽  
Series Analysis

AbstrakFuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia. Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG AbstractFuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia. Keywords – fuzzy time series, time series data, soft computing, IHSG

scholarly journals A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector

2021 ◽  
Author(s):  
Jaydip Sen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Stock Prices ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
Time Processing ◽  
Forecasting Models ◽  
Series Analysis

<p>Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large volume of time series data executing on high-performance hardware and parallel computing architecture. Thus computations which were seemingly impossible to perform a few years back are quite amenable to real-time time processing and effective analysis today. Stock market time series data are large in volume, and quite often need real-time processing and analysis. Thus it is quite natural that research community has focused on designing and developing robust predictive models for accurately forecasting stochastic nature of stock price movements. This work presents a time series decomposition-based approach for understanding the past behavior of the realty sector of India, and forecasting its behavior in future. While the forecasting models are built using the time series data of the realty sector for the period January 2010 till December 2015, the prediction is made for the time series index values for the months of the year 2016. A detailed comparative analysis of the methods are presented with respect to their forecasting accuracy and extensive results are provided to demonstrate the effectiveness of the six proposed forecasting models. </p>

scholarly journals A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector

2021 ◽  
Author(s):  
Jaydip Sen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Stock Prices ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
Time Processing ◽  
Forecasting Models ◽  
Series Analysis

<p>Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large volume of time series data executing on high-performance hardware and parallel computing architecture. Thus computations which were seemingly impossible to perform a few years back are quite amenable to real-time time processing and effective analysis today. Stock market time series data are large in volume, and quite often need real-time processing and analysis. Thus it is quite natural that research community has focused on designing and developing robust predictive models for accurately forecasting stochastic nature of stock price movements. This work presents a time series decomposition-based approach for understanding the past behavior of the realty sector of India, and forecasting its behavior in future. While the forecasting models are built using the time series data of the realty sector for the period January 2010 till December 2015, the prediction is made for the time series index values for the months of the year 2016. A detailed comparative analysis of the methods are presented with respect to their forecasting accuracy and extensive results are provided to demonstrate the effectiveness of the six proposed forecasting models. </p>

scholarly journals A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Parallel Architecture ◽  
High Volume ◽  
Healthcare Sector ◽  
Series Data ◽  
Decomposition Approach ◽  
Series Analysis

<p>Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of fast computing platforms, it has now become possible to effectively and efficiently extract, store, process and analyze high volume of stock market data with diversity in its contents. Availability of complex algorithms which can execute very fast on parallel architecture over the cloud has made it possible to achieve higher accuracy in forecasting results while reducing the time required for computation. In this paper, we use the time series data of the healthcare sector of India for the period January 2010 till December 2016. We first demonstrate a decomposition approach of the time series and then illustrate how the decomposition results provide us with useful insights into the behavior and properties exhibited by the time series. Further, based on the structural analysis of the time series, we propose six different methods of forecasting for predicting the time series index of the healthcare sector. Extensive results are provided on the performance of the forecasting methods to demonstrate their effectiveness. </p>

scholarly journals A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Parallel Architecture ◽  
High Volume ◽  
Healthcare Sector ◽  
Series Data ◽  
Decomposition Approach ◽  
Series Analysis

<p>Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of fast computing platforms, it has now become possible to effectively and efficiently extract, store, process and analyze high volume of stock market data with diversity in its contents. Availability of complex algorithms which can execute very fast on parallel architecture over the cloud has made it possible to achieve higher accuracy in forecasting results while reducing the time required for computation. In this paper, we use the time series data of the healthcare sector of India for the period January 2010 till December 2016. We first demonstrate a decomposition approach of the time series and then illustrate how the decomposition results provide us with useful insights into the behavior and properties exhibited by the time series. Further, based on the structural analysis of the time series, we propose six different methods of forecasting for predicting the time series index of the healthcare sector. Extensive results are provided on the performance of the forecasting methods to demonstrate their effectiveness. </p>

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

scholarly journals Assessment of Online Deliberative Quality: New Indicators Using Network Analysis and Time-Series Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 1187
Author(s):  
Bokyong Shin ◽  
Mikko Rask
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Collective Intelligence ◽  
Series Data ◽  
Online Data ◽  
Series Analysis ◽  
Study Results ◽  
Online Deliberation

Online deliberation research has recently developed automated indicators to assess the deliberative quality of much user-generated online data. While most previous studies have developed indicators based on content analysis and network analysis, time-series data and associated methods have been studied less thoroughly. This article contributes to the literature by proposing indicators based on a combination of network analysis and time-series analysis, arguing that it will help monitor how online deliberation evolves. Based on Habermasian deliberative criteria, we develop six throughput indicators and demonstrate their applications in the OmaStadi participatory budgeting project in Helsinki, Finland. The study results show that these indicators consist of intuitive figures and visualizations that will facilitate collective intelligence on ongoing processes and ways to solve problems promptly.

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.

scholarly journals Highly comparative time-series analysis: the empirical structure of time series and their methods

2013 ◽  
Vol 10 (83) ◽  
pp. 20130048 ◽  
Author(s):  
Ben D. Fulcher ◽  
Max A. Little ◽  
Nick S. Jones
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Scientific Data ◽  
Series Data ◽  
Series Analysis ◽  
Analysis Methods ◽  
Scientific Disciplines ◽  
History Of ◽  
Reduced Representations

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

scholarly journals A New Approach of Fuzzy-Wavelet Method’s Implementation in Time Series Analysis

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Seng Hansun ◽  
Subanar Subanar
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Soft Computing ◽  
Hybrid Model ◽  
Fuzzy Inference ◽  
Series Data ◽  
Wavelet Method ◽  
New Approach ◽  
Series Analysis ◽  
Forecasting Methods

      Abstract— Recently, many soft computing methods have been used and implemented in time series analysis. One of the methods is fuzzy hybrid model which has been designed and developed to improve the accuracy of time series prediction.      Popoola has developed a fuzzy hybrid model which using wavelet transformation as a pre-processing tool, and commonly known as fuzzy-wavelet method. In this thesis, a new approach of fuzzy-wavelet method has been introduced. If in Popoola’s fuzzy-wavelet, a fuzzy inference system is built for each decomposition data, then on the new approach only two fuzzy inference systems will be needed. By that way, the computation needed in time series analysis can be pressed.      The research is continued by making new software that can be used to analyze any given time series data based on the forecasting method applied. As a comparison there are three forecasting methods implemented on the software, i.e. fuzzy conventional method, Popoola’s fuzzy-wavelet, and the new approach of fuzzy-wavelet method. The software can be used in short-term forecasting (single-step forecast) and long-term forecasting. There are some limitation to the software, i.e. maximum data can be predicted is 300, maximum interval can be built is 7, and maximum transformation level can be used is 10. Furthermore, the accuracy and robustness of the proposed method will be compared to the other forecasting methods, so that can give us a brief description about the accuracy and robustness of the proposed method. Keywords—  fuzzy, wavelet, time series, soft computing

Time Series Analysis in Pooled Cross-Sections

1986 ◽  
Vol 2 (3) ◽  
pp. 331-349 ◽  
Author(s):  
John J. Beggs
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Sections ◽  
Spectral Methods ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
Series Analysis ◽  
Simulation Results ◽  
Spectral Representations

This article proposes the use of spectral methods to pool cross-sectional replications (N) of time series data (T) for time series analysis. Spectral representations readily suggest a weighting scheme to pool the data. The asymptotically desirable properties of the resulting estimators seem to translate satisfactorily into samples as small as T = 25 with N = 5. Simulation results, Monte Carlo results, and an empirical example help confirm this finding. The article concludes that there are many empirical situations where spectral methods canbe used where they were previously eschewed.

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