Comparison of the trend moment and double moving average methods for forecasting the number of dengue hemorrhagic fever patients

Khairina, Dyna Marisa; Khairunnisa, Rizka; Hatta, Heliza Rahmania; Maharani, Septya

doi:10.11591/eei.v10i2.2711

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…( 4) calculation. The values obtained from forecasting results with the trend moment method will be corrected for seasonal effects using the season index (Khairina et al, 2021).…”

Section: Trend Momentmentioning

confidence: 99%

Sales Forecasting Analysis Using Trend Moment Method: A Study Case of a Fast Moving Consumer Goods Company in Indonesia

Fauzan¹,

Rahayu²,

Handayani³

et al. 2023

J. Inf. Technol. Cyber Secur.

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The market of Fast-Moving Consumer Goods (FMCG) companies in Indonesia is enormous. Unilever has 400 brands in more than 190 countries, making it a global business that is as influential in the consumer product market as it is in Indonesia. Sales forecasting at this company is very useful for planning expenses and the company's total costs on the business strategy. This study uses trend moment method to forecast the sales and earnings of Unilever Indonesia companies at the end of the year. This article aims to test the performance of the trend moment method calculation on the prediction of net sales and profits in FMCG companies. At the end of the analysis process, it can be concluded that forecasting using trend moment method is going very well. This indicator of success is shown by the error level of MAPE, which is below 10%.

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“…( 4) calculation. The values obtained from forecasting results with the trend moment method will be corrected for seasonal effects using the season index (Khairina et al, 2021).…”

Section: Trend Momentmentioning

confidence: 99%

Sales Forecasting Analysis Using Trend Moment Method: A Study Case of a Fast Moving Consumer Goods Company in Indonesia

Fauzan¹,

Rahayu²,

Handayani³

et al. 2023

J. Inf. Technol. Cyber Secur.

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“…A trend in the data means that the observation values tend to either increase or decrease over time. The double moving average model requires calculating the moving average and then calculating the second moving average using the first moving average values as observations (Khairina et al 2021). The formula used for forecasting in the period t+m of the double moving average method is…”

Section: ) Double Moving Averagementioning

confidence: 99%

A modified weighting system for combined forecasting methods based on the correlation coefficients of the individual forecasting models

Wongoutong

2023

Pak.j.stat.oper.res.

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Herein, a modified weighting for combined forecasting methods is established. These weights are used to adjust the correlation coefficient between the actual and predicted values from five individual forecasting models based on their correlation coefficient values and ranking. Time-series datasets with three patterns (stationary, trend, or both trend and seasonal) were analyzed by using the five individual forecasting models and three combined forecasting methods: simple-average, Bates-Granger, and the proposed approach. The MAPE and RMSE results indicate that the proposed method outperformed the others, especially when the time-series pattern was stationary and improved the forecasting accuracy of the worst and best individual forecasting models by 35–37% and 7–10%, respectively. Moreover, the proposed method showed improvements in MAPE and RMSE of around 18–20% and 9–11% compared to the simple-average and Bates-Granger methods, respectively. In addition, the combined forecasting methods outperformed the individual forecasting models when analyzing non-stationary data. Remarkably, the performances of the proposed and Bates-Granger methods were almost the same, with improvements in MAPE and RMSE in the range of 1–2% on average. Therefore, the proposed method for creating weights based on the correlation coefficients of the individual forecasting models greatly improves combined forecasting methods.

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“…Error measurement is a statistical calculation used to obtain the model error. Here, the mean absolute error, MAE is calculated using (2). It is used to analyze the amount of error in the model sure to different MA window sizes.…”

Section: Error Measurementmentioning

confidence: 99%

“…A large number of data is collected from the home energy management system (HEMS), which also involves different challenges in different data analysis stages. Not only in the smart home field, accurate forecasting is required in different fields, such as Weather forecasting [1], patients number forecasting [2], marketing researches forecasting [3], mortality rates forecasting [4], rainfall forecasting [5], and more. The data can be collected, pre-processed, analyzed, and monitored using predictive analysis (PA), and advance intelligent technologies can help convert these data into reports, charts, and graphs.…”

Section: Introductionmentioning

confidence: 99%

Anomalies detection for smart-home energy forecasting using moving average

Jesmeen

Murthy

Hossen

et al. 2022

IJECE

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In the past few years, the increase in the relation between the physical and digital world over the internet was witnessed. Even though the applications can enhance smart home systems, it is still early stages and challenges in the field of internet of things (IoT). An extreme level of data quality (DQ) system management is essential to produce a meaningful vision. However, in most home energy management system has no straightforward process of removing abnormal data. Hence, the research aims to propose and validate the model of anomaly detection for power consumption in real-time. The moving average (MA) approach identifies and removes abnormal energy consumption data. The results obtained from the forecasting time series auto regressive integrated moving average (ARIMA) model demonstrated that the proposed heuristics effectively enhanced energy usage forecasting. The selection of optimum parameter values for the MA was comprehended for time-series forecasting error minimization by comparing mean squared error (MSE). These outcomes proved the effectiveness of the existing technique and precision of choice of the appropriate. Therefore, the method can effectively route the cleaned sequence data streams in a real-time environment, which is valuable for spotting the anomalies and eliminating for enhancing energy usage time series.

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Comparison of the trend moment and double moving average methods for forecasting the number of dengue hemorrhagic fever patients

Cited by 7 publications

References 20 publications

Sales Forecasting Analysis Using Trend Moment Method: A Study Case of a Fast Moving Consumer Goods Company in Indonesia

Sales Forecasting Analysis Using Trend Moment Method: A Study Case of a Fast Moving Consumer Goods Company in Indonesia

A modified weighting system for combined forecasting methods based on the correlation coefficients of the individual forecasting models

Anomalies detection for smart-home energy forecasting using moving average

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