Optimising the Smoothness and Accuracy of Moving Average for Stock Price Data

Raudys, Aistis; Pabarskaite, Zidrina

doi:10.3846/20294913.2016.1216906

Cited by 36 publications

(25 citation statements)

References 22 publications

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“…The moving average is considered a type of real-time filter that removes high frequencies from data. In signal processing, MAs are therefore also called "low-pass filters" [19] where the calculated coefficients are equal to the reciprocal of the span or bandwidth. Moving averages are also known as "exponential smoothing".…”

Section: Moving Averagementioning

confidence: 99%

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Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

et al. 2021

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The demand for high steady state network traffic utilization is growing exponentially. Therefore, traffic forecasting has become essential for powering greedy application and services such as the internet of things (IoT) and Big data for 5G networks for better resource planning, allocation, and optimization. The accuracy of forecasting modeling has become crucial for fundamental network operations such as routing management, congestion management, and to guarantee quality of service overall. In this paper, a hybrid network forecast model was analyzed; the model combines a non-linear auto regressive neural network (NARNN) and various smoothing techniques, namely, local regression (LOESS), moving average, locally weighted scatterplot smoothing (LOWESS), the Sgolay filter, Robyn loess (RLOESS), and robust locally weighted scatterplot smoothing (RLOWESS). The effects of applying smoothing techniques with varied smoothing windows were shown and the performance of the hybrid NARNN and smoothing techniques discussed. The results show that the hybrid model can effectively be used to enhance forecasting performance in terms of forecasting accuracy, with the assistance of the smoothing techniques, which minimized data losses. In this work, root mean square error (RMSE) is used as performance measures and the results were verified via statistical significance tests.

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Section: Moving Averagementioning

confidence: 99%

“…be the time series where p is the time series length. Therefore, the moving average of the period q at time can be calculated as per ( 10) [19]. In (10) and (11) show the final forecast formula using hybrid moving average and NARNN:…”

Section: Moving Averagementioning

confidence: 99%

Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

et al. 2021

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“…In the same year, Raudys and Pabarškaitė [30] presented a moving average method used to prevent financial signal swing. The determination of the moving average that is appropriate for the financial signal increases the efficiency in the analysis of the stock market.…”

Section: Moving Average Filtermentioning

confidence: 99%

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Palananda

Kimpan

2021

IEEE Access

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In general, considering standard production, as well as coconut oil production, in oil consumption industries is an important factor. Oil color is an important element, as it is an important factor for consumers or buyers in selecting coconut oil. In the process of producing coconut oil, the cold-pressed method has been chosen to maintain the essential quality of coconut oil. The quality of the coconut oil is inspected from the production process by means of light passing through the coconut oil. Then, the production staff compares the turbidity of coconut oil with the master sample. The turbidity of coconut oil in every production must be compared with a master sample to maintain standards control. According to previous studies, there are many methods for determining coconut oil turbidity. One method that has been utilized is determining turbidity from light passing through the medium in which the transmitted light can be absorbed through the turbidity of the variable medium. This process is applied together with image processing to determine the coconut oil turbidity. In this research, we propose a method for measuring coconut oil turbidity by the Moving Average Median of Hue (MAMoH), which is better in detecting the coconut oil turbidity than the Median of Gray Scale (MoGS) method, Median of Hue (MoH) method, and Random Position Median of Hue (RPMoH) method. In terms of the percentage accuracy of the efficiency test; the MAMoH method has 99 percent accuracy, while the MoGS method is not applicable, the MoH method has 88.04 percent accuracy, and the RPMoH method has 85.91 percent accuracy. Thus, the MAMoH method is considered an appropriate method for measuring coconut oil turbidity. INDEX TERMSImage processing, turbidity level, moving average, coconut oil, Computer vision.

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“…Moving Average umumnya digunakan untuk meramalkan tren pada data finansial yang mempunyai noise. Penelitian [11] melakukan optimasi penghalusan (smoothing) Moving Average untuk meningkatkan akurasi analisa harga saham. Semakin besar periode window-nya ( ) akan semakin tinggi reduksi noise-nya dan semakin halus datanya.…”

Section: A Analisa Teknikal Pada Harga Sahamunclassified

Optimasi Parameter Input pada Artificial Neural Network untuk Meningkatkan Akurasi Prediksi Indeks Harga Saham

Agung¹

2021

SISFOKOM

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Stock trading is one of the businesses that has been done worldwide. In order to gain the maximum profit, accurate analysis is needed, so a trader can decide to buy and sell stock at the perfect time and price. Conventionally, two analyses are employed, namely fundamental and technical. Technical analysis is obtained based on historical data that is processed mathematically. Along with technology development, stock price analysis and prediction can be performed with the help of computational algorithms, such as machine learning. In this research, Artificial Neural Network simulations to produce accurate stock price predictions were carried out. Experiments are performed by using various input parameters, such as moving average filters, in order to produce the best accuracy. Simulations are completed with stock index datasets that represent three continents, i.e. NYA (America, USA), GDAXI (Europe, Germany), and JKSE (Asia, Indonesia). This work proposes a new method, which is the utilization of input parameters combinations of C, O, L, H, MA-5 of C, MA-5 of O, and the average of O C prices. Furthermore, this proposed scheme is also compared to previous work done by Khorram et al, where this new work shows more accurate results.

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Optimising the Smoothness and Accuracy of Moving Average for Stock Price Data

Cited by 36 publications

References 22 publications

Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Optimasi Parameter Input pada Artificial Neural Network untuk Meningkatkan Akurasi Prediksi Indeks Harga Saham

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