scholarly journals Clustering Stock Performance Considering Investor Preferences Using a Fuzzy Inference System

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1148 ◽  
Author(s):  
Siti Nazifah Zainol Abidin ◽  
Saiful Hafizah Jaaman ◽  
Munira Ismail ◽  
Ahmad Syafadhli Abu Bakar
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Extreme Values ◽  
Stock Selection ◽  
Actual Performance ◽  
Inference System ◽  
Stock Performance ◽  
Fuzzy Clustering Method ◽  
Rule Bases

The fact that many stocks are traded in the marketplace makes the selection process of choosing the right stocks for investment crucial and challenging. In the literature on stock selection, cluster analysis-based methods have usually been used to group and to determine the best stock for investment. Many established cluster analysis-based methods often cluster stocks under consideration using the average of the variables, where stocks with similar scores are concluded as having the same performances. Nevertheless, the performance results obtained do not reflect the actual performance of the stocks. Depending only on the average score of each variable is inefficient, as market situations usually involve uncertain extreme values. Moreover, when grouping stock performance, the established clustering methods assume that investors’ selection preferences are single and unclear, when actually, in reality, investors’ selection preferences vary; some investors are pessimistic, while others may be more optimistic. Due to this issue, this paper presents a novel fuzzy clustering method using a fuzzy inference system to flexibly assess the consistent evaluations given to stock performance that differentiate between pessimistic and optimistic investors that are symmetrical in nature. All variables considered in this study were defined in terms of linguistic inputs, where the consensus among them was aggregated using rule bases. These rule bases provide assistance in obtaining the linguistic output, which is the actual performance of the stock. Next, each stock under consideration was ranked using the proposed novel stock selection strategy based on investors’ confidence levels and preferences. The proposed method was then applied to a case study of 30 Syariah stocks listed on the Malaysian stock exchange, where the results obtained were empirically validated with established cluster analysis-based methods.

scholarly journals The Fuzzy Inference System with Least Square Optimization for Time Series Forecasting

2018 ◽  
Vol 11 (3) ◽  
pp. 1015 ◽  
Author(s):  
Samingun Handoyo ◽  
Marji Marji
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Square ◽  
Training Data ◽  
Lookup Table ◽  
Rule Base ◽  
Inference System ◽  
Rule Bases ◽  
Takagi Sugeno ◽  
Development And Validation

The rule base on the fuzzy inference system (FIS) has a major role since the output generated by the system is highly dependent on it. The rule base is usually obtained from an expert but in this study proposed the rule base generated based on input-output data pairs with generating rule bases using lookup table scheme, then consequent part of each rule optimized with ordinary least square(OLS), so finally formed rule base from model FIS Takagi-Sugeno orde zero. The exchange rate dataset of EURO to USD is used for the development and validation of the system. In this study, 12 FISs were developed from a combination of linguistic values of n = 3,5,7, 9 with the number of lag (k) assumed to have an effect on output for k = 2,3,5. In training data, values R<sup>2</sup> ranged between 0.989 and 0.993, MAPE values ranged between 0.381% and 0.473% where the FIS with the combination of n = 9 and k = 5 has the best performance. In the testing data, values R<sup>2</sup> ranged between 0.203 and 0.7858, MAPE values ranged between 0.5136% and 0.9457% where FIS n = 3 and k = 2 perform best.

RULE CORRELATION AND CHOQUET INTEGRATION IN FUZZY INFERENCE SYSTEMS

2008 ◽  
Vol 16 (05) ◽  
pp. 601-626 ◽  
Author(s):  
R. A. MARQUES PEREIRA ◽  
R. A. RIBEIRO ◽  
P. SERRA
Keyword(s):  
Fuzzy Inference System ◽  
Correlation Matrix ◽  
Fuzzy Inference ◽  
Integration Scheme ◽  
Rule Base ◽  
Inference System ◽  
Aggregation Scheme ◽  
Inference Systems ◽  
Rule Bases ◽  
Takagi Sugeno

We propose an extension of the Takagi-Sugeno-Kang (TSK) fuzzy inference system, using Choquet integration for aggregating the single rule outputs. In the new Choquet-TSK fuzzy inference system, the pairwise synergies between rules are encoded in a rule correlation matrix computed from the activation pattern of the rule base. The rule correlation matrix is then used to modulate the parameters of the Choquet integration scheme in order to compensate for the effect of rule synergies, which are present in most rule bases to a higher or lesser extent.The standard TSK fuzzy inference system remains a particular instance of the proposed Choquet-TSK extension and corresponds to the ideal case of rule independence. However, when rule correlation is present, the Choquet-TSK fuzzy inference system takes it into account when computing the final output of the system. On the basis of the rule correlation matrix, the new aggregation scheme of the Choquet-TSK fuzzy inference system attenuates the effective weight of positively correlated rules and emphasizes that of negatively correlated rules. Some case studies are discussed in order to illustrate the proposed methodology.

A Method of Optimizing the Rule Base in the Sugeno Fuzzy Inference System Using Fuzzy Cluster Analysis

2020 ◽  
Vol 15 (4) ◽  
pp. 316
Author(s):  
Vladimir Ignatyev ◽  
Andrey Kovalev ◽  
Oleg Spiridonov ◽  
Viktor Kureychik ◽  
Viktor Soloviev ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Cluster ◽  
Rule Base ◽  
Fuzzy Cluster Analysis ◽  
Inference System

scholarly journals MORPHOGENESIS OF RULEBASES OF FUZZY INFERENCE SYSTEMS IN TERMS OF CONTRADICTOR EXPERT OPINIONS

2019 ◽  
pp. 95-105
Author(s):  
Alexander Aleksandrovich Sorokin
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Rule Base ◽  
Expert Group ◽  
Fuzzy Inference Systems ◽  
Output Variable ◽  
Inference System ◽  
Expert Opinions ◽  
Inference Systems ◽  
Rule Bases

The article describes a method of morphogenesis of the rulebase of fuzzy conclusion system in the context of counter-expert opinions. One of the difficulties of the morphogenesis of the rule base, which reflects the result of the collective opinion of the expert group, is processing of counter-narrative conclusions, which are expressed as incompatible rules. The methods used to formulate the rule bases of fuzzy inference systems, in the case of counter-predictive opinions of experts, are based on the removal of incompatible rules, depending on the value of their confidence coefficient. Moreover, the methods for identifying the values of confidence coefficients are not described enough, in addition, the removal of the rules leads to the loss of information about the object that the expert group formed. The proposed method of morphogenesis of rule bases in terms of counter-predictive expert opinions based on the results of the interaction of input variables is based on the identification of the confidence coefficient of each of the rules, depending on the number and level of qualification of the experts who proposed it. To evaluate the effectiveness of the proposed method, a numerical experiment was carried out, based on the study of a typical model for assessing the state of an object, which is used in other examples that demonstrate the principles of the fuzzy inference system. To compare the effectiveness, expert information processing methods containing incompatible rules were used. The performance criterion was the model sensitivity indicator. In the framework of the experiment, sensitivity was understood as the number of various values of the output variable depending on the values of the input parameters. As a result of the experiment, it was shown that the fuzzy inference system using the rule base formed using the proposed method has a noticeably wide variety of input values while maintaining the monotonicity of the change in the values of the output variable. The results of the study allow more advanced methods for identifying the state of elements of socio-economic and organizational-technical systems in which there is terminological uncertainty in the description of critical parameters and the incomplete knowledge of experts on a problem area.

scholarly journals Evaluasi Kinerja pelaksanaan Anggaran Berbasis Fuzzy Inference System

2021 ◽  
Vol 8 (2) ◽  
pp. 333
Author(s):  
Sukarna Sukarna ◽  
Irman Hermadi ◽  
Yani Nurhadryani
Keyword(s):  
Internal Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Performance Status ◽  
Monitoring And Evaluation ◽  
Work Unit ◽  
Actual Performance ◽  
Inference System ◽  
The Status ◽  
Real Performance

<p class="Abstrak">Kinerja Pemerintah merupakan output kinerja kementerian/lembaga yang diimplementasikan dalam Perjanjian Kinerja. Laporan Kinerja adalah bentuk pertanggungjawaban kementerian terhadap Perjanjian Kinerja yang disusun secara bertahap dari unit kerja, unit organisasi, dan kementerian. Capaian kinerja aggaran tingkat Kementerian Pertanian tahun 2019 sebesar 94.56% (kategori sangat baik) namun nilai efisiensi anggaran tingkat kementerian sebesar 71,89% yang disebabkan oleh belum efisiennya penggunaan anggaran terhadap capain target. Penelitian ini menggunakan <em>fuzzy inference system</em>  Mamdani untuk menentukan status capaian kinerja di unit kerja Balai Pengkajian Teknologi Pertanian (BPTP) Jawa Tengah berdasarkan Peraturan Menteri Keuangan nomor 214 tahun 2017. Aspek penilaian terdiri dari aspek manfaat dan aspek implementasi. Hasil penelitian ini adalah monitoring dan evaluasi kinerja tingkat unit kerja berupa status capaian kinerja berdasarkan realisasi anggaran riil dengan kriteria sangat kurang, kurang, cukup, baik, dan baik sekali. Implementasi sistem  berbasis web  dengan nilai evaluasi <em>usability</em> menggunakan <em>use questionnaire</em> untuk masing-masing kategori yaitu US sebesar 5.98, EU sebesar 5.25, EL sebesar 5.75, dan SC sebesar 5.17. Pengujian status capaian kinerja unit kerja than 2019 dengan penilaian pakar menujukkan hasil yang sama yaitu kategori sangat baik.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abtract</strong></em></p><p class="Abstract"><em>Government performance was the output of the performance of the ministry/agency implemented in the performance agreement. Performance report was a form of ministerial accountability to performance agreements that were arranged in stages from work units, organizational units, and ministries.  The achievement of the budget performance at the ministry of agriculture in 2019 was 94.56% (very good category) but the efficiency value of the ministry leveled budget was 71.89% due to the inefficient used of the budget towards target achievement, it is necessary to monitoring and evaluate budget performace.  Measurement and assesment of the achievement of budget execution performance based on PMK Number 214 of 2017 consists of implementation aspects (budget absorption, consistency between planning and implementation, achievement of outputs, and efficiency). The studied objective was to facilitate works units in monitoring and evaluate budget performance by presenting accurate and actual performance information as a function of internal control using  the mamdani fuzzy inference system to determine the status of performance achievements. Mamdan’s model can describe skills for intuitive problems that have an output in the form of values in the domain of fuzzy sets categorized under the linguistic component. The results of this studied are monitoring and evaluation of the web-based work unit in the form of real performance status as a basis for preparing recommendations in  order to improve budget performance.  The usability evaluation of the system used a use questionnaire for each category, namely US 5.98, EU 5.25, EL 5.75, and SC 5.17.  Whitebox testing against 25 rule bases shows the results as expected. Testing the status of work unit performance achievements in 2019 with expert assessments shows the same results, namely the very good category.<strong></strong></em></p><p class="Abstrak" align="center"><em> </em></p>

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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