The Prominence of Stationarity in Time Series Forecasting

Greunen, Jan van; Heymans, André; Heerden, Chris van; Vuuren, Gary van

doi:10.1080/10800379.2014.12097260

Cited by 24 publications

(10 citation statements)

References 22 publications

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“…Before modelling the data, it is necessary to check its stationarity. Van Greunen, Heymans, van Heerden, and van Vuuren (2014) claim that the capacity to render a time series to the correct form of stationarity can lead to false results, while the inability to render a time series to the correct form of stationarity can lead to erroneous results. The statistical features of a process that generates a time series do not vary over time, which is known as stationarity.…”

Section: Proposed Methodologymentioning

confidence: 99%

İstanbul Barajlarının Doluluk Oranı Tahmini

NALİCİ

Akbaş

2022

EJOSAT

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Drinking water is becoming a crucial problem all over the world due to the effects of global warming. In crowded metropolises such as Istanbul, the problem of drinking water is a serious problem. In this study, it is aimed at developing a forecasting model by using the occupancy rates of Istanbul's dams between 2011 and 2020. The occupancy rate of each dam is then estimated using the best model for the years 2021 and 2022. According to the results of the estimation model, a decrease in the occupancy level of the dams in Istanbul is predicted. Therefore, it is thought that necessary measures should be taken to avoid water shortages.

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Section: Proposed Methodologymentioning

confidence: 99%

İstanbul Barajlarının Doluluk Oranı Tahmini

NALİCİ

Akbaş

2022

EJOSAT

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“…In order to apply time series forecasting, stationarity of the time series is required. So to check stationarity of the time series, Augmented Dickey-Fuller test (ADF Test), a common statistical method is performed [ 26 , 27 ], which is one of the most widely used statistical measures when it comes to the study of the stationary sequence. The ADF test results reveal that the dataset for daily confirmed cases and total confirmed cases are non-stationary.…”

Section: Methodsmentioning

confidence: 99%

Predicting mortality rate and associated risks in COVID-19 patients

et al. 2021

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The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and R 2 . During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models.

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“…This implies in particular that a time series has constant mean and variance over time. More detailed definitions about stationarity and its importance in forecasting has been discussed in [van Greunen et al, 2014].…”

Section: Stationaritymentioning

confidence: 99%