Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets

Khashei, Mehdi; Rafiei, Farimah Mokhatab; Bijari, Mehdi

doi:10.1080/18756891.2013.809937

Cited by 23 publications

(14 citation statements)

References 39 publications

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“…The attacker's target is a random choice; as a result we can build a prediction model based on the time series and make use of the existing finite data to predict the future value. There are several basic time series models to choose from: the autoregressive (AR) model, the moving average (MA) model, and the autoregressive moving average (ARMA) model (36).…”

Section: Prediction Of the Next Targeted Clustermentioning

confidence: 99%

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Han

Zhou

Jia

et al. 2019

Information Sciences

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A Wireless Sensor Network (WSN) security strategy is imperative to counteract security threats, and energy consumption directly affects the network life-time of a wireless sensor; thus an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Current Intrusion Detection Systems can detect just some specific attacks and the network life time is reduced due to their high energy consumption. For the purpose of energy limitation and high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model to a non-cooperative complete information static game model, but makes a prediction about attack pattern based on the reliable model. The proposed approach improves on previous approaches in two main ways: it takes energy consumption of the intrusion detection process into account, and it obtains the optimal defense strategy, balancing detection efficiency and the system's energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that the IDS proposed not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.

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Section: Prediction Of the Next Targeted Clustermentioning

confidence: 99%

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Han

Zhou

Jia

et al. 2019

Information Sciences

View full text Add to dashboard Cite

show abstract

“…The Forecasted interval of Fuzzy Auto-Regressive Integrated Moving Average models is extended in some specific data conditions [36]. According to the Ishibuchi and Tanaka opinion, forecasting interval can be too wide, when training data set includes the significant difference or outlying case [35].…”

Section: The Fuzzy Auto-regressive Integrated Moving Average (Farima)mentioning

confidence: 99%

Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting

Khashei

Montazeri²,

Bijari³

2015

IJMSC

Self Cite

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In today's world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. In the literature, Many different time series methods have been frequency compared together in order to choose the most efficient once. In this paper, the performances of four different interval ARIMA-base time series methods are evaluated in financial markets forecasting. These methods are including Auto-Regressive Integrated Moving Average (ARIMA), Fuzzy Auto-Regressive Integrated Moving Average (FARIMA), Fuzzy Artificial Neural Network (FANN) and Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH). Empirical results of exchange rate forecasting indicate that the fuzzy artificial neural network model is more satisfactory than other models.

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“…Given the advantages of ANNs, it is not surprising that this methodology has attracted overwhelming attention in financial markets and especially exchange rate prediction. Numerous researchers have investigated ANNs for forecasting exchange rates and have shown that neural networks can be one of the very useful tools in foreign exchange markets forecasting (Khashei et al , 2013).…”

Section: Introductionmentioning

confidence: 99%

A Kalman filter-based hybridization model of statistical and intelligent approaches for exchange rate forecasting

Khashei

Sharif

2020

JM2

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Purpose The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in order to yield a more general and more accurate hybrid model for exchange rates forecasting. For this purpose, the Kalman filter technique is used in the proposed model to preprocess and detect the trend of raw data. It is basically done to reduce the existing noise in the underlying data and better modeling, respectively. Design/methodology/approach In this paper, ARIMA models are applied to construct a new hybrid model to overcome the above-mentioned limitations of ANNs and to yield a more general and more accurate model than traditional hybrid ARIMA and ANNs models. In our proposed model, a time series is considered as a function of a linear and nonlinear component, so, in the first phase, an ARIMA model is first used to identify and magnify the existing linear structures in data. In the second phase, a multilayer perceptron is used as a nonlinear neural network to model the preprocessed data, in which the existing linear structures are identified and magnified by ARIMA and to predict the future value of time series. Findings In this paper, a new Kalman filter based hybrid artificial neural network and ARIMA model are proposed as an alternate forecasting technique to the traditional hybrid ARIMA/ANNs models. In the proposed model, similar to the traditional hybrid ARIMA/ANNs models, the unique strengths of ARIMA and ANN in linear and nonlinear modeling are jointly used, aiming to capture different forms of relationship in the data; especially, in complex problems that have both linear and nonlinear correlation structures. However, there are no aforementioned assumptions in the modeling process of the proposed model. Therefore, in the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be generally guaranteed that the performance of the proposed model will not be worse than either of their components used separately. In addition, empirical results in both weekly and daily exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Originality/value In the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components used separately. In addition, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternate model for forecasting in exchange ratemarkets, especially when higher forecasting accuracy is needed.

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Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets

Cited by 23 publications

References 39 publications

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting

A Kalman filter-based hybridization model of statistical and intelligent approaches for exchange rate forecasting

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