Gold price forecasting with a neuro-fuzzy-based inference system

Makridou, Georgia; Atsalakis, George S.; Zopounidis, C.; Andriosopoulos, Kostas

doi:10.1504/ijferm.2013.053707

Cited by 19 publications

(9 citation statements)

References 34 publications

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“…Adaptive Neuro Fuzzy Inference System (ANFIS) menggabungkan Fuzzy Logic dan NN dengan tujuan mengambil keuntungan dari dua pendekatan non-linier berbasis machine learning (Faulina & Suhartono, 2013). Makridou et al (2013) memperoleh prediksi harga emas yang dihasilkan ANFIS lebih akurat daripada ARIMA dan NN. ANFIS memiliki komponen yang serupa dengan NN, yaitu memiliki hidden layer.…”

Section: Pendahuluanunclassified

Improvisasi Model Arimax-Anfis Dengan Variasi Kalender Untuk Prediksi Total Transaksi Non-Tunai

Putera

2020

IJSA

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Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.

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Section: Pendahuluanunclassified

Improvisasi Model Arimax-Anfis Dengan Variasi Kalender Untuk Prediksi Total Transaksi Non-Tunai

Putera

2020

IJSA

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“…A number of innovative DSA operational analysis are considered for diversified BI service applications, such as, product life cycle management service that uses closed-loop PLM framework [41], transport logistic service that implements an IoE based ontology framework [42], and a supply chain management service that uses a cognitive based smart logistic framework [43].…”

Section: Context Of Data Science and Analyticsmentioning

confidence: 99%

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Mishra¹

2018

IJSRCSE

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Abstract-The latest internet of everything (IoE) advancements in data elicitation and digital storage technology leads to a large heterogeneous data depository, in which the IoE data are stored in a column oriented relational framework. The main purposes of the research are to design and explore data models, frameworks, architectures, and algorithms on network-centric data, mainly IoE data to accomplish the data science and knowledge analytic tasks for Intellectual domain applications. Some storage incompatibilities are there in the relational structure of multi-objective IoE data base that creates threats to data integrity and consistency. In a large scale IoE database, huge numbers of rows are there along with limited number of columns. So, column oriented relational framework greatly improve the performance of IoE data base in terms of data depository and access management. Knowledge analytic is the major part of data science; Analytic is a never ending process because of progressive technological change requirements as well as the business change requirements. The beauty of Analytics is that two data scientist with same problem may come up with two different new solutions. So, in this work, I discuss the overall data science and knowledge analytic streams for an effective IoE database management and knowledge discovery.

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“…Several text-based or feature-based clustering algorithms have been proposed by data mining researchers [ 35 – 37 ]. However, we present a simple knowledge-clustering mechanism that is based on the associated θ values.…”

Section: Knowledge Granule Analytic and Cluster (Kgac) Frameworkmentioning

confidence: 99%

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

2015

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The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

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Gold price forecasting with a neuro-fuzzy-based inference system

Cited by 19 publications

References 34 publications

Improvisasi Model Arimax-Anfis Dengan Variasi Kalender Untuk Prediksi Total Transaksi Non-Tunai

Improvisasi Model Arimax-Anfis Dengan Variasi Kalender Untuk Prediksi Total Transaksi Non-Tunai

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

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