A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet

Ning, Yanrui; Kazemi, Hossein; Tahmasebi, Pejman

doi:10.1016/j.cageo.2022.105126

Cited by 105 publications

(31 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ning et al [13] present a machine learning-based time series forecasting system that uses existing data as time series and extracts prominent attributes from historical data to predict future time sequence values. Three methods were explored and evaluated to overcome the restrictions of traditional production forecasting: autoregressive integrated moving averages (ARIMA), the long-short-term memory (LSTM) network, and Prophet.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan

Hussein

Mahmoud

Eisa³

2023

EAI Endorsed Trans Smart Cities

View full text Add to dashboard Cite

Today, artificial intelligence is a key tool for turning a city into a smart city, and advances in information and communication technology (ICT) have led to the development of smart cities with many different parts. Smart Health is one of these components and is used to improve healthcare by providing services such as disease forecasting, early diagnosis, and others. There are various machine learning algorithms available now that can help with S-Health services, but which is better for disease forecasting? Gedaref State, for example, has some of Sudan's heaviest rains, and malaria and pneumonia are widespread throughout the year. Predicting future trends for these diseases has been a major focus for researchers in order for Gedaref's administration and the state's ministry of health to design effective ways to prevent and control the development of these diseases, as well as to prepare an adequate stock of medicine. As a result, it is necessary to establish a trustworthy and accurate forecasting model to aid Gedaref's government in developing economic and medical strategies for dealing with these diseases, as well as taking action on medical resource allocation. This study uses a time series dataset collected from the state's ministry of health to estimate malaria and pneumonia as common diseases in Gedaref state, Sudan, five months later. To comprehend the overall number of cases of diseases, two forecasting methodologies, namely the ARIMA and Prophet models, are applied to the disease's dataset. The performance of the ARIMA and FB-Prophet forecasting systems in predicting malaria and pneumonia diseases in Gedaref State is compared in this study. The data was collected from the state's ministry of health between January 2017 and December 2021. The results reveal that the ARIMA technique outperforms the FB-Prophet forecasting method in both malaria (RMSE: 182.8, MAE: 141.6, MAPE: 0.0057, and MASE: 0.0537) and pneumonia (RMSE: 1400.3, MAE: 1001.4, MAPE: 0.0513, and MASE: 0.9136).

show abstract

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan

Hussein

Mahmoud

Eisa³

2023

EAI Endorsed Trans Smart Cities

View full text Add to dashboard Cite

show abstract

“…Box and Gwilym Jenkins in 1976, first introduced the ARIMA model, so this method is commonly known as ARIMA Box-Jenkins. The ARIMA model consists of two aspects of the process; they are the Autoregressive (AR) process and the Moving Average (MA) process, so in general, the ARIMA model is notated as ARIMA(p,d,q), with p stating the order of the AR Process, q stating the order of the MA process, and d stating differencing [14]- [16].…”

Section: B Autoregressive Integrate Moving Average (Arima)mentioning

confidence: 99%

Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters

Annas

Ahmar

Hidayat

2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Unhealthy and unsafe sanitation will make it easier for various diseases to attack the body. In addition, unsafe sanitation will also affect a country's economy, including declining welfare, tourism losses, and environmental losses due to the loss of productive land. The research aimed to estimate the welfare cost of premature deaths based on unsafe sanitation risks using the SutteARIMA, Neural Network Time Series, and Holt-Winters. The study analyzed estimates and projections of the welfare cost of premature deaths based on the risks of unsafe sanitation of BRICS countries (Brazil, Russia, Indonesia, China, and South Africa). The data in this research used secondary data. Secondary time series data was taken from the Environment Database of the OECD. Stat. (Mortality and welfare cost from exposure to environmental risks). The data on the study was based on variables: welfare cost of premature deaths, % GDP equivalent, risk: unsafe sanitation, age: all, sex: both, unit: percentage, and data from 2005 to 2019. The three forecasting methods (SutteARIMA, Neural Network Time Series, and Holt-Winters) were juxtaposed in fitting data to see the forecasting methods' reliability and accuracy. The accuracy of forecasting results was compared based on MAPE and MSE values. The results of the research showed that the SutteARIMA and NNAR(1,1) methods were best used to predict the welfare cost of premature deaths in view of unsafe sanitation risks for BRICS countries.

show abstract

“…This model is widely applied for different purposes than initially intended. The applications included demand forecasting (Kolková & Navrátil, 2021), Taming energy, and electronic waste generation in bitcoin mining (Jana, Ghosh & Wallin, 2022), or oil production prediction (Ning, Kazemi & Tahmasebi, 2022). Wang, Du & Qi (2022), Ning et al (2022) andBasakat et al (2022) verified the feasibility of the Prophet model in practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - the case of SMEs and large enterprises

Kolková¹,

Ključnikov²

2022

Economics &Amp; Sociology

View full text Add to dashboard Cite

Demand forecasting is one of the biggest challenges of post-pandemic logistics. It appears that logistics management based on demand prediction can be a suitable alternative to the just-in-time concept. This study aims to identify the effectiveness of AI-based and statistical forecasting models versus practice-based models for SMEs and large enterprises in practice. The study compares the effectiveness of the practice-based Prophet model with the statistical forecasting models, models based on artificial intelligence, and hybrid models developed in the academic environment. Since most of the hybrid models, and the ones based on artificial intelligence, were developed within the last ten years, the study also answers the question of whether the new models have better accuracy than the older ones. The models are evaluated using a multicriteria approach with different weight settings for SMEs and large enterprises. The results show that the Prophet model has higher accuracy than the other models on most time series. At the same time, the Prophet model is slightly less computationally demanding than hybrid models and models based on artificial neural networks. On the other hand, the results of the multicriteria evaluation show that while statistical methods are more suitable for SMEs, the prophet forecasting method is very effective in the case of large enterprises with sufficient computing power and trained predictive analysts.

show abstract

A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet

Cited by 105 publications

References 7 publications

Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan

Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan

Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters

Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - the case of SMEs and large enterprises

Contact Info

Product

Resources

About