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

Khashei, Mehdi; Montazeri, Mohammad Ali; Bijari, Mehdi

doi:10.5815/ijmsc.2015.01.03

Cited by 12 publications

(8 citation statements)

References 41 publications

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“…Now we plot the error terms from each model into a single plot. Figure 17 and 18 43 respectively. The average value of error MAPE for Naï ve, Drift, SES, Holt, Holt Damped, ETS, ARIMA and neural network is 5.06 %, 4.35%, 4.39%, 5.41%, 4.58%, 5.38%, 3.61% and 4.26% respectively.…”

Section: Cross-validation and Error Analysismentioning

confidence: 95%

“…Reference [21]define ARIMA forecasting model represented in terms of past values of itself including current and lagged values of a 'white noise' error term. Reference [42] analyzed the data of given parameters and noticed their predictions for a particular period by using the strategy of Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS) .TThe performances of four different interval ARIMA-base time series methods are evaluated in financial markets forecasting [43].…”

Section: Related Workmentioning

confidence: 99%

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Comparative Analysis of Univariate Forecasting Techniques for Industrial Natural Gas Consumption

Naim¹,

Mahara²

2018

IJIGSP

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This paper seeks to evaluate the appropriateness of various univariate forecasting techniques for providing accurate and statistically significant forecasts for manufacturing industries using natural gas. The term "univariate time series" refers to a time series that consists of single observation recorded sequentially over an equal time interval. A forecasting technique to predict natural gas requirement is an important aspect of an organization that uses natural gas in form of input fuel as it will help to predict future consumption of organization.We report the results from the seven most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the Naive method. Naï ve method, Drift method, Simple Exponential Smoothing (SES), Holt method, ETS(Error, trend, seasonal) method, ARIMA, and Neural Network (NN) have been studied and compared.Forecasting accuracy measures used for performance checking are MSE, RMSE, and MAPE. Comparison of forecasting performance shows that ARIMA model gives a better performance.

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Section: Cross-validation and Error Analysismentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Univariate Forecasting Techniques for Industrial Natural Gas Consumption

Naim¹,

Mahara²

2018

IJIGSP

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show abstract

“…In [14], four different interval ARIMA-base time series methods were used for financial markets forecasting. The used methods are: Fuzzy Auto-Regressive Integrated Moving Average (FARIMA), Fuzzy Artificial Neural Network (FANN), ARIMA and Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH).…”

Section: Related Workmentioning

confidence: 99%

Forecasting using Artificial Neural Network and Statistics Models

Al-Maqaleh¹,

Al-Mansoub²,

Al-Badani³

2016

IJEME

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Forecasting is very important for planning and decision-making in all fields to predict the conditions and cases surrounding the problem under study before making any decision. Hence, many forecasting methods have been developed to produce accurate predicted values. Consumer price indices provide appropriate and timely information about prices changes, which affect the economy of all Yemenis because of their different uses in many ways. It can be used as an economic indicator (wider use in the inflation measurement), and as a means of regulating income. It is also used as a supplement for statistical chains to predict future value indices in order to make sure that the data accurately reflect the patterns purchased by the Yemeni consumer. In this paper, we propose a modified artificial neural network method to predict the indices of consumer in the Republic of Yemen to the prices of the period from 01/01/2005 till 01/01/2014. The results of using the proposed method is compared to a classical statistical method. The proposed method is based on artificial neural networks, namely, back propagation with adaptive slope and momentum parameter to update weights. However, the statistical method is Box-Jenkins model which is used to predict time series. The experimental results show that artificial neural networks gives better predictive values due to their ability to deal with the nonlinear and stochastic data better than traditional statistical modeling techniques.

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“…Jiang and Li [21] designed a composite forecasting framework that combined feature extraction and fuzzy set theory selection technique to estimate the prediction intervals of wind speed time series through the machine learning method embedding a multi-objective salp swarm algorithm. Khasheia et al [22] compared the four different confidence interval ARIMA-based time series methods in financial markets forecasting. Kim et al [15] utilize AR, BOOT (AR model using the bias-corrected bootstrap), SARIMA, ETS (state-space exponential smoothing model), and ST (Harvey's structural time series models) to estimate confidence intervals of the passenger arrivals in Hong Kong and Macao.…”

Section: Introductionmentioning

confidence: 99%

An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction

Chang

Lee

2022

IEEE Access

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Developing an effective interval-valued time series (ITS) forecasting scheme for electric power generation is an important issue for energy operators and governments when making energy strategic decisions. The existing studies for ITS forecasting only consider basic descriptive information such as center, radius, upper and lower bounds, and overlooks the distribution information within the data interval. In this study, an interval-valued time series forecasting scheme based on probability distribution information features of interval-valued data with machine learning algorithms is proposed to enhance electric power generation forecasting. In the proposed scheme, the central tendency features and dispersion features from the interval-valued data are designed as integrated features sets (IFS) and used as predictor variables. Three methods including supper vector regression and extreme learning machine and multivariate adaptive regression splines based on the IFS are utilized to develop ITS forecasting models. The daily time series of the metered generation from the Australian Energy Market Operator is used to illustrate the proposed scheme. Empirical results show that the proposed ITS forecasting schemes with IFS outperform the eight benchmark models and thus validate that the proposed scheme is an effective alternative for interval-valued electric power generation forecasting.

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

Cited by 12 publications

References 41 publications

Comparative Analysis of Univariate Forecasting Techniques for Industrial Natural Gas Consumption

Comparative Analysis of Univariate Forecasting Techniques for Industrial Natural Gas Consumption

Forecasting using Artificial Neural Network and Statistics Models

An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction

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