AN EXPONENTIALLY WEIGHTED MOVING AVERAGE METHOD WITH DESIGNED INPUT DATA ASSIGNMENTS FOR FORECASTING LIME PRICES IN THAILAND

2017 ◽  
Vol 79 (6) ◽  
Author(s):  
Thitima Booranawong ◽  
Apidet Booranawong
Keyword(s):  
Input Data ◽  
Moving Average ◽  
Weighting Factor ◽  
Percentage Error ◽  
Exponentially Weighted Moving Average ◽  
Monthly Data ◽  
Forecasting Error ◽  
The Mean ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In this paper, the Exponentially Weighted Moving Average (EWMA) method with designed input data assignments (i.e. the proposed method) is presented to forecast lime prices in Thailand during January 2016 to December 2016. The lime prices from January 2011 to December 2015 as the input data are gathered from the website’s database of Simummuang market, which is one of the big markets in Thailand. The novelty of our paper is that although the performance of the EWMA method significantly decreases when applying to forecast data which show trend and seasonality behaviors and the EWMA method is used for short-range forecasting (i.e. usually one month into the future), the proposed method can properly handle such mentioned problems. For this purpose, to forecast lime prices, five different input data are intently defined before assigned to the EWMA method: a) the monthly data of the year 2015 (i.e. the recent year data), b) the average monthly data of the year 2011 to 2015, c) the median of the monthly data of the year 2011 to 2015, d) the monthly data of the year 2011 to 2015 after applying the linear weighting factor, where the higher weight value is applied to the recent data, and e) the average monthly data of the year 2011 to 2015 after applying the exponential weighting factor, where the higher weight is also applied to the recent data. These designed input data are used as agents of the raw data. Our study reveals that using the input data b) with the EWMA method to forecast lime prices during January 2016 to September 2016 gives the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE). Forecasted lime prices of October 2016 to December 2016 are also provided. Additionally, we demonstrate that the proposed method works well compared with the Double Exponentially Weighted Moving Average (DEWMA), the Multiplicative Holt-Winters (MHW), and the Additive Holt-Winters (AHW) methods, which are suitably used for forecasting data with the trend and the seasonality.

Progressive mean exponentially weighted moving average control chart for monitoring the process location

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nurudeen Ayobami Ajadi ◽  
Osebekwin Asiribo ◽  
Ganiyu Dawodu
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Performance Measure ◽  
Exponentially Weighted Moving Average ◽  
Average Scheme ◽  
Content Type ◽  
The Mean ◽  
Memory Type ◽  
Weighted Moving Average ◽  
Exponentially Weighted

PurposeThis study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.Design/methodology/approachThe PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.FindingsThe variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.Originality/valueA new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

On designing a robust double exponentially weighted moving average control chart for process monitoring

2018 ◽  
Vol 40 (15) ◽  
pp. 4253-4265 ◽  
Author(s):  
Ishaq Adeyanju Raji ◽  
Nasir Abbas ◽  
Muhammad Riaz
Keyword(s):  
Moving Average ◽  
Real Life ◽  
Exponentially Weighted Moving Average ◽  
Data Set ◽  
Parameter Estimations ◽  
Real Life Data ◽  
Monitoring Process ◽  
The Mean ◽  
Weighted Moving Average ◽  
Exponentially Weighted

A double exponentially weighted moving average chart has been proven more efficient for monitoring process mean in comparison to the classical exponentially weighted moving average chart. We, in this article, made a careful investigation on how well this scheme performs with the presence of disturbances in the process under consideration. This investigation was motivated in exploring the scheme with some robust statistic, as the mean estimator performs woefully. We also evaluated the effects of parameter estimation on the phase II assuming the parameters are unknown. Adopting a 20% trimmed mean of trimeans (robust) reveals the effect of parameter estimations. We substantiated these claims by applying the scheme on a real-life data set. The findings of the study pronounced the trimean estimator to be the best of all the five estimators used, including the mean.

A SYNTHETIC CONTROL CHART FOR MONITORING THE PROCESS MEAN OF SKEWED POPULATIONS BASED ON THE WEIGHTED VARIANCE METHOD

2008 ◽  
Vol 15 (03) ◽  
pp. 217-245 ◽  
Author(s):  
MICHAEL B. C. KHOO ◽  
ZHANG WU ◽  
ABDU M. A. ATTA
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Synthetic Control ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Variance Method ◽  
The Mean ◽  
Synthetic Chart ◽  
Weighted Moving Average ◽  
Exponentially Weighted

A synthetic control chart for detecting shifts in the process mean integrates the Shewhart [Formula: see text] chart and the conforming run length chart. It is known to outperform the Shewhart [Formula: see text] chart for all magnitudes of shifts and is also superior to the exponentially weighted moving average chart and the joint [Formula: see text]-exponentially weighted moving average charts for shifts of greater than 0.8σ in the mean. A synthetic chart for the mean assumes that the underlying process follows a normal distribution. In many real situations, the normality assumption may not hold. This paper proposes a synthetic control chart to monitor the process mean of skewed populations. The proposed synthetic chart uses a method based on a weighted variance approach of setting up the control limits of the [Formula: see text] sub-chart for skewed populations when process parameters are known and unknown. For symmetric populations, however, the limits of the new [Formula: see text] sub-chart are equivalent to that of the existing [Formula: see text] sub-chart which assumes a normal underlying distribution. The proposed synthetic chart based on the weighted variance method is compared by Monte Carlo simulation with many existing control charts for skewed populations when the underlying populations are Weibull, lognormal, gamma and normal and it is generally shown to give the most favourable results in terms of false alarm and mean shift detection rates.

scholarly journals Time dependent prediction of monthly global solar radiation and sunshine duration using exponentially weighted moving average in southeastern of Turkey

2018 ◽  
Vol 22 (2) ◽  
pp. 943-951 ◽  
Author(s):  
Bilal Gumus ◽  
Hibetullah Kilic
Keyword(s):  
Solar Radiation ◽  
Sunshine Duration ◽  
Moving Average ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Eastern Region ◽  
Exponentially Weighted Moving Average ◽  
Weighted Moving Average ◽  
Exponentially Weighted

This paper proposes a new approach for prediction of global solar radiation and sunshine duration based on earlier years of data for the eastern region of Turkey which has a high potential of solar energy. The proposed method predicts the basic parameters using time series and an analysis method. This method is exponentially weighted moving average. This model estimates next years global solar radiation and sunshine duration and is evaluated by statistical parameters, mean absolute percentage error (MAPE) and coefficient of determination, to examine the success of the proposed technique. In our study, the result shows that this method is effective in predicting global solar radiation and sunshine duration as regards of MAPE and coefficient of determination. The calculated MAPE which are between 0-10 kWh/m2 per day were assumed excellent and coefficient of determination were found significant per every year.

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