Double Exponential-Smoothing Neural Network for Foreign Exchange Rate Forecasting

Muladi,; Siregar, Sherly Allsa; Wibawa, Aji Prasetya

doi:10.1109/eiconcit.2018.8878591

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…The testing procedure would use data on the number of unique visitors to an electronic journal from September 13, 2018, to December 31, 2018. After testing the data, the next stage was to evaluate the forecasting results and assess the method's efficacy by calculating the error value [28]. The error value was calculated using MSE, as shown in (4), and RMSE, as shown in ( 5) [48].…”

Section: Discussionmentioning

confidence: 99%

“…The BPNN approximates data patterns and makes accurate predictions by repeating this procedure [24]. Pattern identification [25], image processing [26], time-series forecasting [27], and financial prediction [28] This work attempts to solve this research gap by presenting an innovative method that combines mean and median smoothing approaches to preprocess data and strengthen the model's robustness to boost BPNN's performance, increasing the accuracy of unique visitor predictions. This study was carried out in order to fulfill these goals.…”

Section: Literature Reviewmentioning

confidence: 99%

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Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Wibawa

2023

J. Appl. Data Sci.

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Unique visitors are first-time IP address visitors in a certain time window, a significant indicator of an electronic journal's performance and accreditation. This study uses a backpropagation neural network (BPNN) to improve visitor prediction. From January 1, 2018, to December 31, 2018, the KEDS.csv file on the page contained page views, sessions, visitors, and new visitors. The data is preprocessed using mean and median smoothing. MSE and RMSE are examined and compared to the BPNN model without smoothing and the original data. The BPNN model with mean smoothing, MSE 0.00129, and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture has the lowest error. Integrating mean and median smoothing techniques considerably enhances the BPNN forecasting model. Mean, and median smoothing lowers data volatility and noise, making predictions more accurate. The BPNN model with smoothing has lower MSE and RMSE than the model without smoothing and the original data. This work is unusual in combining mean and median smoothing with a BPNN model to predict unique visits to electronic journals. This research advances time series forecasting by predicting electronic journal visiting patterns. The literature benefits from evaluating smoothing strategies and their effects on predicting. The study helps electronic journal practitioners evaluate visitor patterns and journal performance by boosting prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Wibawa

2023

J. Appl. Data Sci.

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“…In addition, they developed the DES method for efficient jitter compensation, which showed that DES-based schemes ran about 100 times faster than Extended Kalman Filter (EKF)-based methods and 19 times faster than Kalman Filter (KF)-based methods. Siregar and Wibawa [4] compared the DES and ANN methods with the SES process on data input for foreign currency exchange, and found that the MAPE results were 53% with an execution time of 561 seconds. Therefore, it can be concluded that the DES method is better than SES for improving ANN performance for forecasting foreign currency exchange rates.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Ginger Harvest Yields: A Comparative Study of Double Exponential Smoothing and Long Short-Term Memory Models

Anamisa,

Mufarroha,

Jauhari

et al. 2024

MMEP

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“…Efforts to see situations and conditions in the future is an attempt to estimate the effect that applies to future developments which is called forecasting [2]. Forecasting is an important tool in effective planning [3] and is widely used in various fields [4][5] [6]. Evaluation of the use of the system implemented by the company is important [7], but forecasting is also needed to increase the quality of business strategy.…”

Section: Introductionmentioning

confidence: 99%

Building The Prediction of Sales Evaluation on Exponential Smoothing using The OutSystems Platform

Arnomo,

Yulia,

Ukas

2023

Ilk. J. Ilm.

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To get a large profit in a company or business is to determine sales predictions for the next period. Prediction or forecasting is one of the keys to the success of sales because the predicted value of sales can be used as a reference to determine the order of goods, so there is no loss. Exponential smoothing method is a fairly superior forecasting method in long-term, medium-term and short-term forecasting. The data to be processed is sales data for the 2020-2022 period. The single exponential smoothing method was chosen because it can determine sales predictions for the next period with the smallest error value. The evaluation method used is MAPE, ME, MAD and MSE where this forecasting method is used to find the smallest error value. Based on the calculation results, the smallest error value obtained is ME at 62.8, MAD at 179.9, MSE at 55564.5, and MAPE at 9.20%. The value is at alpha 0.3. The next stage is to design a prediction system using the out-systems platform version 11.14.1 as a place to design the system. The test results of the system that has been designed to assist business owners in making decisions on product inventory estimates.

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Double Exponential-Smoothing Neural Network for Foreign Exchange Rate Forecasting

Cited by 6 publications

References 8 publications

Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Forecasting Ginger Harvest Yields: A Comparative Study of Double Exponential Smoothing and Long Short-Term Memory Models

Building The Prediction of Sales Evaluation on Exponential Smoothing using The OutSystems Platform

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