Some Properties of a Simple Moving Average when Applied to Forecasting a Time Series

Johnston, F. R.; Boyland, J. E.; Meadows, M.; Shale, E.

doi:10.2307/3010636

Cited by 16 publications

(12 citation statements)

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“…Simple moving average Simple moving average (SMA) is a low-pass filter which replaces every point by unweighted mean of around points in a span (Johnston et al 1999;Kirchgassner and Jurgen 2007;Mac Berthouex and Brown 1994;Wei 1994). The smoothing signal is given by: where x(i) is the smoothed value, N denotes number of neighboring points on each side, and 2N + 1 represents the span.…”

Section: Regression-based Smoothing Methodsmentioning

confidence: 99%

A comparison of methods for denoising of well test pressure data

Moosavi

Qajar

Riazi

2018

J Petrol Explor Prod Technol

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Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS-ARMA to real field data shows promising results.

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Section: Regression-based Smoothing Methodsmentioning

confidence: 99%

A comparison of methods for denoising of well test pressure data

Moosavi

Qajar

Riazi

2018

J Petrol Explor Prod Technol

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“…where is the value at time period , is EWMA value at time , and is a constant smoothing factor with a range value between 0 and 1. As suggested by Johnston et al [14], can be estimated as:…”

Section: A Exponentially Weighted Moving Averagementioning

confidence: 99%

Human Development Index Forecasting using Exponentially Weighted Moving Average

2019

ijeat

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Human Development Index (HDI) is an indicator to see the ruling government achievement, especially in the human development aspect. It was introduced by United Nations Development Programme (UNDP) in their 1990 report and integrated three basic dimensions of human development, i.e., the ability to lead a long and healthy life, to acquire knowledge, and to achieve a decent standard of living. Indonesia itself was ranked 113 out of 188 UNDP countries and ranked fifth among ASEAN countries for HDI value based on UNDP Human Development Re-port 2016: Human Development for Everyone. In this study, we are trying to forecast Indonesia’s HDI using Exponentially Weighted Moving Average (EWMA) method. EWMA is a popular and widely used method to smooth out random fluctuations. Based on the experiments conducted in 34 provinces in Indonesia, EWMA has been successfully applied to forecast the human development index in Indonesia. It has an average score of MSE and MAPE values at 1.002279153 for MSE and 1.374754513% for MAPE.

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“…The only attempt to develop a statistical model underlying the SMA was given by Johnston et al (1999). The authors discuss the SMA and the SES and show that the SMA can be connected to a steady-state model, but do not discuss this model in details, focusing on variances calculations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This shows that the derived formula (2) is approximate and can only give a general understanding of how the two methods can be connected. Johnston et al (1999) conduct the simulation experiment, but never evaluate the performance of the SMA on the real data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Old dog, new tricks: a modelling view of simple moving averages

Svetunkov

Petropoulos

2017

International Journal of Production Research

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Simple moving average (SMA) is a well-known forecasting method. It is easy to understand and interpret and easy to use, but it does not have an appropriate length selection mechanism and does not have an underlying statistical model. In this paper we show two statistical models underlying SMA and demonstrate that the automatic selection of the optimal length of the model can easily be done using this finding. We then evaluate the proposed model on a real dataset and compare its performance with other popular simple forecasting methods. We find that SMA performs better both in terms of point forecasts and prediction intervals in cases of normal and cumulative values.

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Some Properties of a Simple Moving Average when Applied to Forecasting a Time Series

Cited by 16 publications

References 0 publications

A comparison of methods for denoising of well test pressure data

A comparison of methods for denoising of well test pressure data

Human Development Index Forecasting using Exponentially Weighted Moving Average

Old dog, new tricks: a modelling view of simple moving averages

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