Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

10.21203/rs.3.rs-1074872/v1 ◽

2021 ◽

Author(s):

Zekai Lu ◽

Nian Liu ◽

Ying Xie ◽

Junhui Xu

Keyword(s):

Machine Learning ◽

Time Series ◽

Statistical Models ◽

Energy Production ◽

Production Data ◽

Learning Models ◽

Energy Research ◽

Public Health Emergencies ◽

Production Plans ◽

Machine Learning Models

Abstract Covid-19 was a huge catastrophe for the whole world, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and Covid-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the machine learning models LightGBM, NNAT and LSTM worked better in the unstable energy series, while the statistical model ARIMA still had an advantage in the stable energy time series. Overall, the machine learning models outperformed the traditional models in Covid-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during Covid-19.

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Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

10.21203/rs.3.rs-1074872/v2 ◽

2021 ◽

Author(s):

Zekai Lu ◽

Nian Liu ◽

Ying Xie ◽

Junhui Xu

Keyword(s):

Machine Learning ◽

Time Series ◽

Statistical Models ◽

Energy Production ◽

Production Data ◽

Learning Models ◽

Energy Research ◽

Public Health Emergencies ◽

Production Plans ◽

Machine Learning Models

Abstract Covid-19 was a huge catastrophe for the whole world, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and Covid-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the machine learning models LightGBM, NNAT and LSTM worked better in the unstable energy series, while the statistical model ARIMA still had an advantage in the stable energy time series. Overall, the machine learning models outperformed the traditional models in Covid-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during Covid-19.

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A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA

Neural Computing and Applications ◽

10.1007/s00521-016-2766-x ◽

2016 ◽

Vol 30 (5) ◽

pp. 1519-1527 ◽

Cited By ~ 8

Author(s):

Volkan Ülke ◽

Afsin Sahin ◽

Abdulhamit Subasi

Keyword(s):

Machine Learning ◽

Time Series ◽

Empirical Evidence ◽

Learning Models ◽

Inflation Forecasting ◽

The Usa ◽

Machine Learning Models

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Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

Journal of Crohn s and Colitis ◽

10.1093/ecco-jcc/jjab155 ◽

2021 ◽

Author(s):

Nghia H Nguyen ◽

Dominic Picetti ◽

Parambir S Dulai ◽

Vipul Jairath ◽

William J Sandborn ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Risk Prediction ◽

Statistical Models ◽

Prediction Models ◽

Risk Of Bias ◽

Learning Models ◽

Bowel Diseases ◽

Inflammatory Bowel ◽

Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

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Discriminating Postural Control Behaviors from Posturography with Statistical Tests and Machine Learning Models: Does Time Series Length Matter?

Lecture Notes in Computer Science - Computational Science – ICCS 2018 ◽

10.1007/978-3-319-93713-7_28 ◽

2018 ◽

pp. 350-357

Author(s):

Luiz H. F. Giovanini ◽

Elisangela F. Manffra ◽

Julio C. Nievola

Keyword(s):

Machine Learning ◽

Time Series ◽

Postural Control ◽

Statistical Tests ◽

Learning Models ◽

Series Length ◽

Machine Learning Models

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Intra-domain and cross-domain transfer learning for time series

10.5194/egusphere-egu21-12142 ◽

2021 ◽

Author(s):

Erik Otović ◽

Marko Njirjak ◽

Dario Jozinović ◽

Goran Mauša ◽

Alberto Michelini ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Transfer Learning ◽

Time Series Data ◽

The Other ◽

Series Data ◽

Sound Recognition ◽

Transfer Of Knowledge ◽

Learning Models ◽

Machine Learning Models

In this study, we compared the performance of machine learning models trained using transfer learning and those that were trained from scratch - on time series data. Four machine learning models were used for the experiment. Two models were taken from the field of seismology, and the other two are general-purpose models for working with time series data. The accuracy of selected models was systematically observed and analyzed when switching within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). In seismology, we used two databases of local earthquakes (one in counts, and the other with the instrument response removed) and a database of global earthquakes for predicting earthquake magnitude; other datasets targeted classifying spoken words (speech), predicting stock prices (finance) and classifying muscle movement from EMG signals (medicine). In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model. Therefore, in our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic such conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that used transfer learning for training and those that were trained from scratch. We compared the performances between pairs of models in order to draw conclusions about the utility of transfer learning. In order to confirm the validity of the obtained results, we repeated the experiments several times and applied statistical tests to confirm the significance of the results. The study shows when, within the set experimental framework, the transfer of knowledge brought improvements in terms of model accuracy and in terms of model convergence rate. Our results show that it is possible to achieve better performance and faster convergence by transferring knowledge from the domain of global earthquakes to the domain of local earthquakes; sometimes also vice versa. However, improvements in seismology can sometimes also be achieved by transferring knowledge from medical and audio domains. The results show that the transfer of knowledge between other domains brought even more significant improvements, compared to those within the field of seismology. For example, it has been shown that models in the field of sound recognition have achieved much better performance compared to classical models and that the domain of sound recognition is very compatible with knowledge from other domains. We came to similar conclusions for the domains of medicine and finance. Ultimately, the paper offers suggestions when transfer learning is useful, and the explanations offered can provide a good starting point for knowledge transfer using time series data.

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An intelligent hybridization of ARIMA with machine learning models for time series forecasting

Knowledge-Based Systems ◽

10.1016/j.knosys.2019.03.011 ◽

2019 ◽

Vol 175 ◽

pp. 72-86 ◽

Cited By ~ 23

Author(s):

Domingos S. de O. Santos Júnior ◽

João F.L. de Oliveira ◽

Paulo S.G. de Mattos Neto

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Forecasting ◽

Learning Models ◽

Machine Learning Models

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Multi-step Time Series Forecasting of Electric Load Using Machine Learning Models

Artificial Intelligence and Soft Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-91253-0_15 ◽

2018 ◽

pp. 148-159 ◽

Cited By ~ 7

Author(s):

Shamsul Masum ◽

Ying Liu ◽

John Chiverton

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Forecasting ◽

Electric Load ◽

Learning Models ◽

Machine Learning Models

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Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms

Mathematics ◽

10.3390/math8122205 ◽

2020 ◽

Vol 8 (12) ◽

pp. 2205

Author(s):

Luis Alfonso Menéndez García ◽

Fernando Sánchez Lasheras ◽

Paulino José García Nieto ◽

Laura Álvarez de Prado ◽

Antonio Bernardo Sánchez

Keyword(s):

Machine Learning ◽

Time Series ◽

Moving Average ◽

Environmental Pollutants ◽

Multivariate Adaptive Regression Splines ◽

Support Vector ◽

Learning Models ◽

Vector Autoregressive ◽

Benzene Concentration ◽

Machine Learning Models

Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

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