Performance analysis of conventional moving average methods in forex forecasting

Hansun, Seng; Kristanda, Marcel Bonar

doi:10.1109/icon-sonics.2017.8267814

Cited by 27 publications

(18 citation statements)

References 23 publications

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“…EMA also puts a greater weight to more recent data, but in exponentially manner. As described in [17], EMA for time series can be calculated recursively using…”

Section: Wma and Ema As Building Block Methodsmentioning

confidence: 99%

Indonesia’s Export-Import Prediction: A Hybrid Moving Average Approach

2018

JESR

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Indonesia as one of the largest countries in Asia Pacific region plays a crucial role in international trading. Since both the import and export performance have become key variables in economic growth of a nation, the urgent to have a proper prediction and analysis of those variables has become a major issue. In this study, we try to address the issue by using a relatively new hybrid moving average approach, i.e. the WEMA (Weighted Exponential Moving Average) method. Using different parameters, it has been shown that WEMA approach could predict Indonesia's export-import future values. Based on MAPE (Mean Absolute Percentage Error) and MASE (Mean Absolute Scaled Error) values as forecast error measurement criteria, WEMA gives the best performance in predicting Indonesia's export weight in kg unit.

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“…EMA also puts a greater weight to more recent data, but in exponentially manner. As described in [17], EMA for time series can be calculated recursively using…”

Section: Wma and Ema As Building Block Methodsmentioning

confidence: 99%

Indonesia’s Export-Import Prediction: A Hybrid Moving Average Approach

2018

JESR

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“…Sebaliknya, semakin kecil nilai k, semakin besar pula bobot yang diberikan pada data terbaru. Penentuan nilai k dilakukan dengan cara penentuan nilai k kecil yang digunakan pada saat terjadi fluktuasi mendadak pada suatu data, sedangkan nilai k besar digunakan pada fluktuasi data yang lebar dan jarang [12].…”

Section: A Analisis Teknikal Sahamunclassified

Prediksi Harga Saham Menggunakan Metode Brown’s Weighted Exponential Moving Average dengan Optimasi Levenberg-Marquardt

Putri

Prasetijo

Rochim

2021

JNTETI

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Saham merupakan surat berharga sebagai tanda atas bagian kepemilikan suatu perusahaan. Kepemilikan saham diperdagangkan di bursa saham untuk perusahaan terbuka. Pasar modal merupakan sebuah kegiatan yang mewadahi keinginan untuk berinvestasi. Trader mencari keuntungan dengan memanfaatkan pergerakan saham yang selalu mengalami perubahan fluktuatif dengan cara menggunakan analisis teknikal. Timbul masalah tentang cara trader memutuskan membeli sebuah saham dengan harga yang tidak pasti. Metode Brown’s Weighted Exponential Moving Average memiliki kelebihan yaitu tingkat akurasi yang lebih baik yang dirasa mampu untuk membantu masalah prediksi harga saham. Algoritme Levenberg-Marquardt memiliki kelebihan mengoptimasi akurasi sehingga diusulkan digunakan sebagai metode optimasi untuk memprediksi harga saham. Adanya prediksi harga saham dengan bantuan metode B WEMA dengan Optimasi LM dapat meningkatkan akurasi prediksi harga saham dibandingkan B-WEMA tanpa optimasi, sehingga mengurangi risiko trading dan meningkatkan persentase keberhasilan. Hasil menunjukkan selisih antara harga nyata dengan prediksi minimum sebesar 4,03%, error terkecil dari MSE sebesar 719,56, dan MAPE sebesar 1,99%.

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“…For the first one, frequency, phase and amplitude are usually used to extract some meaningful features of time series. For the second one, it mainly covers time-domain analysis methods currently available linear Auto-Regressive (AR) models [47], [48], Moving Average (MA) models [49], [50], and Auto-Regressive Moving Average (ARMA) models [51].…”

Section: A Time Series Analysismentioning

confidence: 99%

Characteristic Representation of Stock Time Series Based on Trend Feature Points

Zhou

Yang

et al. 2020

IEEE Access

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Stocks are the most active part of the securities market, and the analysis of stock generally starts from the price fluctuation. Stock trading data have the characteristics of time series, which make it possible to record the transaction prices in a time-evolving order. Due to the large data and high research complexity of time series, some ideal data are difficult to obtain for analyzing and predicting stock movements. Aiming at the problem, we utilize the piecewise linear representation which combines turning points and maximum absolute deviation points in stock time series to extract sequence features. Firstly, the proposed method finds turning points that satisfy the condition given in this paper, and defines the point distance formula to calculate the fitting errors between the subsequence segment and the fitted straight line, whose average value is set as the threshold P and the subsequence length is as the threshold d. Secondly, if the fitting error or the length of the subsequence segment is greater than P or d, respectively, the maximum absolute deviation point is obtained according to the difference between the fitted value and the original data. Finally, all trend feature points are connected by linear interpolation. In this paper, different sequence lengths, different thresholds d, different methods and industrial data from different fields are discussed and compared in detail to verify the proposed method. The experimental results show that the proposed method gets satisfactory data fitting and expanding effect, and retains the characteristics and integrity of the initial time series.INDEX TERMS Data mining, piecewise linear representation, stock time series, trend feature point. I. INTRODUCTION A. BACKGROUND

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Performance analysis of conventional moving average methods in forex forecasting

Cited by 27 publications

References 23 publications

Indonesia’s Export-Import Prediction: A Hybrid Moving Average Approach

Indonesia’s Export-Import Prediction: A Hybrid Moving Average Approach

Prediksi Harga Saham Menggunakan Metode Brown’s Weighted Exponential Moving Average dengan Optimasi Levenberg-Marquardt

Characteristic Representation of Stock Time Series Based on Trend Feature Points

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