A Study of Time Series Models ARIMA and ETS

Jain, Garima; Mallick, Bhawna

doi:10.2139/ssrn.2898968

Cited by 55 publications

(27 citation statements)

References 4 publications

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“…32 , 33 The purpose of using this package is to fit best the ARIMA model to univariate time series and returns best ARIMA model according to either Akaike Information Criterion (AIC), or its small-sample equivalent (AICc) or Bayesian Information Criterion (BIC) value. 34 , 35 The function conducts a search over possible model 36 within the order constraints provided. 1 In Table 1 , the details of the model with corresponding AIC values are documented.…”

Section: Design and Methodsmentioning

confidence: 99%

Prediction of Daily Covid-19 Cases in European Countries Using Automatic Arima Model

Awan

Aslam

2020

Journal of Public Health Research

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The recent pandemic (COVID-19) emerged in Wuhan city of China and after causing a lot of destruction there recently changed its epicenter to Europe. There are countless people affected and reported cases are increasing day by day. Predictive models need to consider previous reported cases and forecast the upcoming number of cases. Automatic ARIMA, one of the predictive models used for forecasting contagions, was used in this study to predict the number of confirmed cases for next 10 days in four top European countries through R package “forecast”. The study finds that Auto ARIMA applied on the sample satisfactorily forecasts the confirmed cases of coronavirus for next ten days. The confirmed cases for the four countries show an increasing trend for the next ten days with Spain with a highest number of expected new confirmed cases, followed by Germany and France. Italy is expected to have lowest number of new confirmed cases among the four countries.

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Section: Design and Methodsmentioning

confidence: 99%

Prediction of Daily Covid-19 Cases in European Countries Using Automatic Arima Model

Awan

Aslam

2020

Journal of Public Health Research

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“…There are no significant results in the comparison with another time series prediction methods with the use of linear data such as the ARIMA and FARIMA (Fuzzy ARIMA) methods, or nonlinear data with the ANN and ANFIS methods, but the use of the ARIMA method is suitable for short-term time prediction [15]. In the other case of research on weather forecasts, the comparison of ARIMA and Exponential Smoothing (ETS) methods illustrates good performance and reasonable prediction accuracy [16]. ARIMA can predict future observations from time series based on several linear functions of past values and white noise based on predetermined basic rules.…”

Section: Forecasting Time Series Modelmentioning

confidence: 99%

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

Verano

Husnawati

Ermatita

2020

JITeCS

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The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

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“…For example, in [74], the authors developed an ARIMA model to forecast several water quality variables like the pH, color (TCU), turbidity (ppm), Al 3+ (ppm), Fe 2+ (ppm), NH 4+ (ppm) and Mn 2+ (ppm) through the respective hydrological variables, namely rainfall and river discharge, for the Johor River in Malaysia. In [75], it is also emphasized that weather parameters such as humidity, wind speed, rainfall or air temperature are nonlinear and complex phenomena involving mathematical simulation and proper modeling for correct forecasting. Aside from ARIMA, the authors also used Exponential Smoothing (ETS) models to forecast the exemplified parameters.…”

Section: Deep Learning Approach In Aquatic Ecosystemsmentioning

confidence: 99%

A Forecasting and Prediction Methodology for Improving the Blue Economy Resilience to Climate Change in the Romanian Lower Danube Euroregion

Petrea

Zamfir²,

Simionov

et al. 2021

Sustainability

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European Union (EU) policy encourages the development of a blue economy (BE) by unlocking the full economic potential of oceans, seas, lakes, rivers and other water resources, especially in member countries in which it represents a low contribution to the national economy (under 1%). However, climate change represents a main barrier to fully realizing a BE. Enabling conditions that will support the sustainable development of a BE and increase its climate resiliency must be promoted. Romania has high potential to contribute to the development of the EU BE due to its geographic characteristics, namely the presence of the Danube Delta–Black Sea macrosystem, which is part of the Romanian Lower Danube Euroregion (RLDE). Aquatic living resources represent a sector which can significantly contribute to the growth of the BE in the RLDE, a situation which imposes restrictions for both halting biodiversity loss and maintaining the proper conditions to maximize the benefits of the existing macrosystem. It is known that climate change causes water quality problems, accentuates water level fluctuations and loss of biodiversity and induces the destruction of habitats, which eventually leads to fish stock depletion. This paper aims to develop an analytical framework based on multiple linear predictive and forecast models that offers cost-efficient tools for the monitoring and control of water quality, fish stock dynamics and biodiversity in order to strengthen the resilience and adaptive capacity of the BE of the RLDE in the context of climate change. The following water-dependent variables were considered: total nitrogen (TN); total phosphorus (TP); dissolved oxygen (DO); pH; water temperature (wt); and water level, all of which were measured based on a series of 26 physicochemical indicators associated with 4 sampling areas within the RLDE (Brăila, Galați, Tulcea and Sulina counties). Predictive models based on fish species catches associated with the Galati County Danube River Basin segment and the "Danube Delta” Biosphere Reserve Administration territory were included in the analytical framework to establish an efficient tool for monitoring fish stock dynamics and structures as well as identify methods of controlling fish biodiversity in the RLDE to enhance the sustainable development and resilience of the already-existing BE and its expansion (blue growth) in the context of aquatic environment climate variation. The study area reflects the integrated approach of the emerging BE, focused on the ocean, seas, lakes and rivers according to the United Nations Agenda. The results emphasized the vulnerability of the RLDE to climate change, a situation revealed by the water level, air temperature and water quality parameter trend lines and forecast models. Considering the sampling design applied within the RLDE, it can be stated that the Tulcea county Danube sector was less affected by climate change compared with the Galați county sector as confirmed by water TN and TP forecast analysis, which revealed higher increasing trends in Galați compared with Tulcea. The fish stock biodiversity was proven to be affected by global warming within the RLDE, since peaceful species had a higher upward trend compared with predatory species. Water level and air temperature forecasting analysis proved to be an important tool for climate change monitoring in the study area. The resulting analytical framework confirmed that time series methods could be used together with machine learning prediction methods to highlight their synergetic abilities for monitoring and predicting the impact of climate change on the marine living resources of the BE sector within the RLDE. The forecasting models developed in the present study were meant to be used as methods of revealing future information, making it possible for decision makers to adopt proper management solutions to prevent or limit the negative impacts of climate change on the BE. Through the identified independent variables, prediction models offer a solution for managing the dependent variables and the possibility of performing less cost-demanding aquatic environment monitoring activities.

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A Study of Time Series Models ARIMA and ETS

Cited by 55 publications

References 4 publications

Prediction of Daily Covid-19 Cases in European Countries Using Automatic Arima Model

Prediction of Daily Covid-19 Cases in European Countries Using Automatic Arima Model

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

A Forecasting and Prediction Methodology for Improving the Blue Economy Resilience to Climate Change in the Romanian Lower Danube Euroregion

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