ModisCfied Holt s Linear Trend Method

Yapar, Guckan; Çapar, Sedat; Selamlar, Hanife Taylan; Yavuz, İdil

doi:10.15672/hjms.2017.493

Cited by 16 publications

(14 citation statements)

References 14 publications

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“…We need a strategy that can delineate pattern precisely with no presumptions. Such a strategy that considers the pattern of the dataset is called Holt’s linear trend technique (Yapar et al 2018 ). Each time arrangement dataset can be disintegrated into its components which are trend, irregularity, and residual.…”

Section: Methodsmentioning

confidence: 99%

Application of machine learning time series analysis for prediction COVID-19 pandemic

Chaurasia

Pal

2020

Res. Biomed. Eng.

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Purpose Coronavirus disease is an irresistible infection caused by the respiratory disease coronavirus 2 (SARS-CoV-2). It was first found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. During pandemic prevention, this can minimize the impact of the disease on individuals and groups. A study was carried out on coronavirus to observe the number of cases, deaths, and recovery cases worldwide within a specific time period of 5 months. Based on this data, this research paper will predict the future spread of this infectious disease in human society. Methods In our study, the dataset was taken from WHO "Data WHO Coronavirus Covid-19 cases and deaths-WHO-COVID-19-global-data". This dataset contains information about the observation date, provenance/state, country/region, and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt-Winters method and ARIMA, for comparison, and how these methods improve the Root mean square error score. Results The naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-fit model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2021. Conclusion This survey will support the government and experts in making arrangements for what is about to happen. Based on the findings of instantaneous model, these models can be adjusted to guide long time.

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Section: Methodsmentioning

confidence: 99%

Application of machine learning time series analysis for prediction COVID-19 pandemic

Chaurasia

Pal

2020

Res. Biomed. Eng.

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“…We need a methodology that can portray design correctly without any assumptions. Such a system that considers the example of the dataset is called Holt's Linear Trend procedure [13]. Each Time plan dataset can be broken down into its segments which are Trend, Irregularity and Residual.…”

Section: Holt's Linear Trend Methodsmentioning

confidence: 99%

COVID-19 Pandemic: Application of Machine Learning Time Series Analysis for Prediction of Human Future

Chaurasia

Pal

2020

SSRN Journal

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Coronavirus disease is an irresistible infection caused by the respiratory disease Coronavirus 2 (SARS-CoV-2). It was rst found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. Prevention is the only cure for this disease. A study was carried out on Coronavirous to observe the number of cases, deaths and recovery cases worldwide within a speci c time period of ve months. Based on this data, this research paper will predict the future spread of this infectious disease in human society. Methods: In our study, the data set was taken from WHO "Data WHO Coronavirus Covid-19 cases and deaths-WHO-COVID-19-global-data". This dataset contains information about the observation date, provenance/state, country/region and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt Winter method and ARIMA, for comparison, and how these methods improve the Root mean square error score. Results: The naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-t model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2020. Conclusion: This survey will support the government and experts in making arrangements for what is about to happen. Based on the ndings of instantaneous model, these models can be adjusted to guide long time.

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“…On the other hand; forecast competitions have played a critical role in both showcasing the effects of these drawbacks on accuracy of forecasts and in helping develop new forecasting techniques that could perform well while forecasting large numbers of time series. Recently, ATA method has been proposed which is a new forecasting method that is simple, fast and accurate [27]- [29]. ATA method is very similar to exponential smoothing in mathematical form but its weighting scheme is quite different.…”

Section: Ata Methodsmentioning

confidence: 99%

Kesi̇kli̇ Talepleri̇n Tahmi̇nlenmesi̇nde Ata Metot Ve Croston Temelli̇ Metotlarin Karşilaştirilmasi

Yilmaz

Yapar

Yavuz

2019

Mugla Journal of Science and Technology

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Intermittent demand forecasting is crucial for firms and commercial activities. Recently, many researchers have focused on forecasting methods for intermittent demand and proposed various forecasting techniques. The most prominent methods among these proposed techniques are the Croston method, which is based on exponential smoothing, and its two popular variations: SBA (Syntetos-Boylan Approximation), SBJ (Shale-Boylan-Johnston Approximation). Croston method is widely used in forecasting of intermittent demand and inventory (stock) control. Since these demands usually include zero values, using the ground breaking method developed by Croston in this data becomes inevitable. Nevertheless, there are some shortcomings to this method such as producing biased forecasts and for this reason its variations have been proposed. ATA method is a recently developed forecasting method which is an alternative to exponential smoothing. In this paper we propose a modification of ATA method that can be used for forecasting of intermittent demand. We will compare the results of the proposed approach to those of Croston and other forecasting methods used for intermittent demand forecasting.

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ModisCfied Holt s Linear Trend Method

Cited by 16 publications

References 14 publications

Application of machine learning time series analysis for prediction COVID-19 pandemic

Application of machine learning time series analysis for prediction COVID-19 pandemic

COVID-19 Pandemic: Application of Machine Learning Time Series Analysis for Prediction of Human Future

Kesi̇kli̇ Talepleri̇n Tahmi̇nlenmesi̇nde Ata Metot Ve Croston Temelli̇ Metotlarin Karşilaştirilmasi

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