Claim Missing Document
Check
Articles

Found 7 Documents
Search

Brown’s Weighted Exponential Moving Average Implementation in Forex Forecasting Seng Hansun; Subanar Subanar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.5410

Abstract

In 2016, a time series forecasting technique which combined the weighting factor calculation formula found in weighted moving average with Brown’s double exponential smoothing procedures had been introduced. The technique is known as Brown’s weighted exponential moving average (B-WEMA), as a new variant of double exponential smoothing method which does the exponential filter processes twice. In this research, we will try to implement the new method to forecast some foreign exchange, or known as forex data, including EUR/USD, AUD/USD, GBP/USD, USD/JPY, and EUR/JPY data. The time series data forecasting results using B-WEMA then be compared with other conventional and hybrid moving average methods, such as weighted moving average (WMA), exponential moving average (EMA), and Brown’s double exponential smoothing (B-DES). The comparison results show that B-WEMA has a better accuracy level than other forecasting methods used in this research.
Simulation of queue with cyclic service in signalized intersection system Muhammad Dermawan Mulyodiputro; Subanar Subanar
International Journal of Advances in Intelligent Informatics Vol 1, No 1 (2015): March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v1i1.15

Abstract

The simulation was implemented by modeling the queue with cyclic service in the signalized intersection system. The service policies used in this study were exhaustive and gated, the model was the M/M/1 queue, the arrival rate used was Poisson distribution and the services rate used was Exponential distribution. In the gated service policy, the server served only vehicles that came before the green signal appears at an intersection. Considered that there were 2 types of exhaustive policy in the signalized intersection system, namely normal exhaustive (vehicles only served during the green signal was still active), and exhaustive (there was the green signal duration addition at the intersection, when the green signal duration at an intersection finished). The results of this queueing simulation program were to obtain characteristics and performance of the system, i.e. average number of vehicles and waiting time of vehicles in the intersection and in the system, as well as system utilities. Then from these values, it would be known which of the cyclic service policies (normal exhaustive, exhaustive and gated) was the most suitable when applied to a signalized intersection system
Development of a Spatial Path-Analysis Method for Spatial Data Analysis Wiwin Sulistyo; Subanar Subanar; Reza Pulungan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1162.002 KB) | DOI: 10.11591/ijece.v8i4.pp2456-2467

Abstract

Path analysis is a method used to analyze the relationship between independent and dependent variables to identify direct and indirect relationship between them. This method is developed by Sewal Wright and initially only uses correlation analysis results in identifying the variables' relationship. Path analysis method currently is mostly used to deal with variables with non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to develop path analysis method so as to identify the effects of spatial dependencies. This paper proposes a method in the form of path analysis method development to process data that have spatial elements. This study also discusses our effort on establishing a method that could be used to identify and analyze the spatial effect on data in the framework of path analysis; we call this method spatial path analysis.
H-WEMA: A New Approach of Double Exponential Smoothing Method Seng Hansun; Subanar Subanar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.3096

Abstract

A popular smoothing technique commonly used in time series analysis is double exponential smoothing. Basically, it’s an improvement of simple exponential smoothing which does the exponential filter process twice. Many researchers had developed the technique, hence Brown’s double exponential smoothing and Holt’s double exponential smoothing. Here, we introduce a new approach of double exponential smoothing, called H-WEMA, which combines the calculation of weighting factor in weighted moving average with Holt’s double exponential smoothing method. The proposed method will then be tested on Jakarta Stock Exchange (JKSE) composite index data. The accuracy and robustness level of the proposed method will then be examined by using mean square error and mean absolute percentage error criteria, and be compared to other conventional methods.
SSA-based hybrid forecasting models and applications Winita Sulandari; Subanar Subanar; Suhartono Suhartono; Herni Utami; Muhammad Hisyam Lee; Paulo Canas Rodrigues
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.132 KB) | DOI: 10.11591/eei.v9i5.1950

Abstract

This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural Network) and TLCSNN (Two-Level Complex Seasonal Neural Network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural networks. Trend and harmonic function were considered as the deterministic part identified based on SSA decomposition. Meanwhile, NN was intended to handle the nonlinearity relationship in the stochastic part. These two SSA-based hybrid models were contemplated to be more flexible than TLSAR and more applicable to the series with an intricate pattern. The experimental studies to the monthly accidental deaths in USA and daily electricity load Jawa-Bali showed that the proposed SSA-based hybrid model reduced RMSE for the testing data from that obtained by TLSAR model up to 95%.
Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model Winita Sulandari; Subanar Subanar; Suhartono Suhartono; Herni Utami
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.69

Abstract

Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast
EFFECTS OF CALENDAR VARIATIONS ON THE INDONESIA STOCK EXCHANGE: AN EMPIRICAL STUDY OF POTENTIAL STOCKS Putriaji Hendikawati; Subanar Subanar; Abdurakhman Abdurakhman; Tarno Tarno
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 4, No 1 (2022)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v4i1.14921

Abstract

This study examines the effect of calendar variations on potential stocks on the Indonesia Stock Exchange. Calendar variations are observed in telecommunications, retail, food and cigarettes sub-sectors. The observed calendar variations are divided into two: the holiday effect, namely the effect of the month of Ramadan, the effect of the Eid al-Fitr holiday, and the effect of changes in the month of the Eid holidays; and the trading day effect, namely the effect of the day of the week and month of the year effects. ARIMA and ARIMAX model is used to see the effect of previous return data and the calendar variations on predicting stock returns. Descriptively, there is the effect of calendar variations due to Ramadan and Eid holidays and the influence of Monday and January effect. The existence of calendar variations does not apply equally to all types of stocks and to all observation time periods. The calendar variation tends to vary, does not form a clear pattern, does not consistently affect stock returns on the Indonesia Stock Exchange and is not statistically significant. Based on the analysis, it was found that the Monday effect and January effect are the most common phenomena in the Indonesian stock exchange.