Comparison of ANFIS, ANN, GARCH and ARIMA Techniques to Exchange Rate Forecasting

Fahimifar, S. M.; Homayounif, M.; Sabouhi, M; Moghaddamn, A. R.

doi:10.3923/jas.2009.3641.3651

Cited by 29 publications

(14 citation statements)

References 20 publications

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“…Model Autoregressive Conditional Heteroscedasticity (ARCH) diusulkan oleh Engle (1982) dan model Generalized Autoregressive Conditional Heteroscedasticity (GARCH) yang dikembangkan oleh Bollerslev (1986) merupakan model varians yang populer. Model ARIMA-GARCH telah banyak diterapkan pada penelitian terkait dengan prediksi data runtun waktu nonlinear Fahimifard et al (2009). Model tersebut masih mempunyai beberapa kelemahan ketika digunakan untuk prediksi data non-linear, karena runtun waktu tersebut tidak cocok dengan fenomena teoritis yang dan juga tidak dapat menangkap etakpastian data.…”

Section: Pendahuluanunclassified

“…Pada beberapa tahun terakhir, model alternatif telah dikembangkan untuk menganalisis data runtun waktu nonlinear antara lain Neural Networks (NN) oleh Fausset (1994); Haykin (1999)), fuzzy system dan hibridanya (Jang et al, 1997). ANFIS yang menggabungkan antara NN dan fuzzy system telah diimplementasikan dalam berbagai bidang penelitian runtun waktu antara lain aplikasi ANFIS berdasarkan singular spectrum analysis untuk prediksi chaotic time series oleh Abdollahzade et al (2015); prediksi chaotic time series menggunakan ANFIS oleh Behmanesh et al (2014); prediksi runtun waktu fuzzy oleh Cheng et al (2016); pengembangan pendekatan baru untuk prediksi kenaikan harga minyak (Mombeini et al, 2014); computational intelligence untuk prediksi chaotic time series; pemodelan temperatur minimum oleh Daneshmand et al (2015); prediksi return saham oleh Wei et al (2011); prediksi volatilitas finansial oleh Luna and Ballini (2012); prediksi nilai tukar valuta asing oleh Fahimifard et al (2009); dan prediksi fluktuasi tinggi danau menggunakan model ensembles ANN dan ANFIS (Talebizadeh, 2011). Sebagian besar penelitian tersebut menyimpulkan bahwa ANFIS lebih baik dari metode yang lain.…”

Section: Pendahuluanunclassified

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Pemodelan Hybrid Arima-Anfis Untuk Data Produksi Tanaman Hortikultura Di Jawa Tengah

Tarno

Rusgiyono

Warsito

et al. 2018

Medstat

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Section: Pendahuluanunclassified

Pemodelan Hybrid Arima-Anfis Untuk Data Produksi Tanaman Hortikultura Di Jawa Tengah

Tarno

Rusgiyono

Warsito

et al. 2018

Medstat

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“…During training, network's weights and biases are iteratively adjusted to minimize the network performance function using mean squares error, mse. The training algorithm used in our study is Leverberg-Marquardt (trainlm) because this algorithm appears to be the fastest method for training moderate-sized feed-forward neural networks and it is also very efficient (Fahimifard et al, 2009). We then apply an early stopping technique to avoid overtraining the neural networks and to improve generalization of the networks.…”

Section: Ann Applicationmentioning

confidence: 99%

Neural Network in Modeling Malaysian Oil Palm Yield

Ismail¹,

Khamis

2011

American Journal of Applied Sciences

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Problem statement: Forecasting of palm oil yield has become an important element in the management of oil palm industry for proper planning and decision making. The importance of yield forecasting has led us to explore modeling of palm oil yield for Malaysia using the most recent development of Artificial Neural Network (ANN). The main issue in yield forecasting is to predict the future value with the minimum error. Approach: Artificial neural networks are computing systems containing many interconnected nonlinear neurons, capable of extracting linear and nonlinear regularity in a given data set. It is an artificial intelligence model originally designed to replicate the human brain's learning process, a network with many elements or neurons that are connected by communications channels or connectors. The ANN can perform a particular function when certain values are assigned to the connections or weights between elements. In this study, a secondary data set from the Malaysian Palm Oil Board (MPOB) on the foliar nutrient composition, fertilizer trials and Fresh Fruit Bunch (FFB) yield were taken and analyzed. The foliar nutrient composition variables are the nitrogen N, phosphorus P, potassium K, calcium Ca and magnesium Mg concentration, while the fertilizer trials data are the N, P, K and Mg fertilizers and are measured in kg per palm per year. The foliar composition data was presented in the form of measured values whiles the fertilizer data in ordinal levels, from zero to three. Results: Two experiments were conducted to demonstrate the implementation ANN and for both experiment, the result demonstrated that the number of hidden nodes produces an effect to the overall forecast performance of the ANN architecture. From the first experiment, it shows that the number of runs does not affect the ANN performance, but changing the momentum to learning rates, due to shows a significant improvement in the forecast result. The experimental result will be in the form of statistical analysis, the best neural network performance, the residual analysis and the effect on the learning rate on the NN performance. Conclusion: This study showed that modeling of oil palm yield using neural network requires data to be prepared or modified to satisfy the requirement of the parameters involved. This analysis yields the conclusion that only the number of hidden nodes has a significant influence on the NN performance and there is no effect resulting from the number of runs or the momentum term value on the neural network's performance.

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“…The volatility model that called generalized autoregressive conditional heteroscedasticity (GARCH) proposed by Bollerslev [5]. Application of hybrid ARIMA-GARCH has been done in research for non-linear and non-stationary data [6]. Unfortunately, the model still has an advantages, because it cannot capture the uncertainty when implemented for non-linear and non-stationary data [7,8].…”

Section: Introductionmentioning

confidence: 99%

Construction of ANFIS Model Based on LM-Test for Forecasting of Chili Price Data in Semarang

Tarno¹,

Maruddani²,

Rahmawati³

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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The research aim is constructing Adaptive Neuro-Fuzzy Inference System (ANFIS) model for forecasting time series data. The ANFIS model is constructed and applied to chili price data in Semarang. The daily data are written during December 2018 to May 2019. The input selection in ANFIS is done by using the Lagrange Multiplier (LM) test. The lag-1 with 2 membership functions is selected as optimal input. The performance of prediction based on in-sample data is measured by the values of mean absolute percentage error (MAPE) and root mean squares error (RMSE). The values of MAPE and RMSE are 2.9% and 939.8 respectively.

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Comparison of ANFIS, ANN, GARCH and ARIMA Techniques to Exchange Rate Forecasting

Cited by 29 publications

References 20 publications

Pemodelan Hybrid Arima-Anfis Untuk Data Produksi Tanaman Hortikultura Di Jawa Tengah

Pemodelan Hybrid Arima-Anfis Untuk Data Produksi Tanaman Hortikultura Di Jawa Tengah

Neural Network in Modeling Malaysian Oil Palm Yield

Construction of ANFIS Model Based on LM-Test for Forecasting of Chili Price Data in Semarang

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