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A Methodology for Calculating the Contribution of Exogenous Variables to ARIMAX Predictions

Hao Wang, Raymond Yao, Likun Hou, Jie Zhao, Xing Zhao
2021 Proceedings of the Canadian Conference on Artificial Intelligence  
Here we argue that the regression coefficients of exogenous variables are not sufficient to measure their contribution to the predictions due to the dynamic nature of the stochastic process in ARIMAX models  ...  Autoregressive integrated moving average with exogenous variables (ARIMAX) is a prevailing model in time series forecasting, yet little attention has been paid to explain the predictions of ARIMAX, which  ...  Acknowledgements We would like to acknowledge the support of SAP devX and SAP HANA.  ... 
doi:10.21428/594757db.2c2969c0 fatcat:yzjrun2favc65otegoehumbwzy

Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods

Robert Basmadjian, Amirhossein Shaafieyoun, Sahib Julka
2021 Energies  
To generate accurate predictions, a methodology is proposed, which consists of two main phases.  ...  To this end, the three most-relevant time-series auto-regression based methods of SARIMAX, SARIMA, and ARIMAX are considered.  ...  To confirm the suitability of those variables as well as to evaluate the contribution of those exogenous variables to the methods, a sensitivity analysis is performed.  ... 
doi:10.3390/en14217443 fatcat:duebo35h3baqtghq7v43qsadza

Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation

Erdong Zhao, Jing Zhao, Liwei Liu, Zhongyue Su, Ning An
2015 Energies  
The ARIMAX model chooses the wind speed result from the Weather Research and Forecasting (WRF) simulation as an exogenous input variable. Further, an SA strategy is applied to the ARIMAX process.  ...  This paper develops a self-adaptive (SA) auto-regressive integrated moving average with exogenous variables (ARIMAX) model that is optimized very-short-term by the chaotic particle swarm optimization (  ...  Acknowledgments: This research was supported by the National Natural Science Foundation of China under Grant (71171102/G0107). Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en9010007 fatcat:ejs26kguynhjjp6sd2ykfd7r4y

Day-ahead industrial load forecasting for electric RTG cranes

2018 Journal of Modern Power Systems and Clean Energy  
The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well.  ...  However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge.  ...  Acknowledgements The authors are grateful to the Port of Felixstowe for providing the electrified RTG cranes data.  ... 
doi:10.1007/s40565-018-0394-4 fatcat:wow4b6nicrew5bewsud7m5soya

Methodology for Security Analysis of Grid- Connected Electric Vehicle Charging Station With Wind Generating Resources

Gyeongmin Kim, Jin Hur
2021 IEEE Access  
The proposed algorithm can be used to analyze power system problems that may occur due to the concentration of electric vehicle charging demand in the future, and to prepare a method for decentralizing  ...  In addition, wind power outputs prediction was performed using the ARIMAX model. Input variables are wind power measurement data and additional explanatory variables (wind speed).  ...  The input variables used in the ARIMAX model comprises both endogenous and exogenous variables.  ... 
doi:10.1109/access.2021.3075072 fatcat:su4uqaodkrgmjg6uezpqenqz3a

Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case

Julio Barzola-Monteses, Mónica Mite-León, Mayken Espinoza-Andaluz, Juan Gómez-Romero, Waldo Fajardo
2019 Sustainability  
Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions.  ...  The results showed that the best model is the ARIMAX (1,1,1) (1,0,0)12, which considers an exogenous variable precipitation in the Napo River basin and can accurately predict monthly production values  ...  Juan Gómez-Romero is partially supported by the University of Granada (P9-2014-ING) and the Spanish Ministries of Science, Innovation and Universities (TIN2017-91223-EXP) and Economy and Competitiveness  ... 
doi:10.3390/su11236539 fatcat:5odrtwuexnetrjj45iwkyn2yde

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Feras Alasali, Husam Foudeh, Esraa Mousa Ali, Khaled Nusair, William Holderbaum
2021 Energies  
The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.  ...  The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges.  ...  Acknowledgments: The authors are grateful to the engineering staff at the National Electric Power. Grid Co (NEPCO) for supporting and collecting data which were used in this paper.  ... 
doi:10.3390/en14082151 fatcat:ysddqsmd5ncvxdptposanxhkui

Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

Marko Intihar, Tomaž Kramberger, Dejan Dragan
2017 Promet (Zagreb)  
For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used.  ...  The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020).  ...  This gives us a total of 66 external exogenous variables.  ... 
doi:10.7307/ptt.v29i5.2334 fatcat:r6wtf2eccnbvrbm6i6dar6w2ru

Prediction the Crime Motorcycles of Theft using ARIMAX-TFM with Single Input

Azhari, Pradita Eko Prasetyo Utomo
2018 2018 Third International Conference on Informatics and Computing (ICIC)  
This study proposes the development of a computer application model to predict the rate of the crime of motorcycle theft with an approach to take into account external influences by using ARIMAX -transfer  ...  This causes difficulties for the police to control and monitor on a regular basis since it requires forecasting and probabilities of theft in a particular time period.  ...  ARIMAX method is not only able to predict the number of cases of theft of motorcycles but also the influence independent variable for example the number of vehicles. II.  ... 
doi:10.1109/iac.2018.8780520 fatcat:wwptmzcwibhv5cgu6y5owsbuhq

Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

Ernest Kissi, Theophilus Adjei-Kumi, Peter Amoah, Jerry Gyimah
2018 Construction Economics and Building  
The results showed that ARIMAX model has a better predictive ability than the use of the single approach.  ...  Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling TPI.  ...  For a given dependent variable and exogenous variable the ARIMAX model can be denoted as: Before proceeding with the estimation of ARIMAX model, stationary analysis of the time series data was carried  ... 
doi:10.5130/ajceb.v18i1.5604 fatcat:ofhukr6albewzbyg2f6hpzpgpu

Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques

Ana E. Sipols, Rubén Valcarce-Diñeiro, Maria Teresa Santos-Martín, Nilda Sánchez, Clara Simón de Blas
2022 Remote Sensing  
This paper aims to both fit and predict crop biophysical variables with a SAR image series by performing a factorial experiment and estimating time series models using a combination of forecasts.  ...  The model equations showed a positive contribution of meteorological variables and a strong temporal component in the crop's development, as occurs in natural conditions.  ...  For field variables where the ARIMAX methodology did not provide a good fit, a robust regression was used to model them instead.  ... 
doi:10.3390/rs14030614 fatcat:tzuycyuud5bi5bsvoykullaede

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

Henrique do Nascimento Camelo, Paulo Sérgio Lucio, João Verçosa Leal Junior, Daniel von Glehn dos Santos, Paulo Cesar Marques de Carvalho
2018 Atmosphere  
To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation).  ...  wind speed predictions for the northeast region of Brazil.  ...  In order to evaluate if the models of the Box-Jenkins and Box-Tiao methodologies are feasible for wind speed prediction, a residue analysis was performed.  ... 
doi:10.3390/atmos9020077 fatcat:raucr6hr5jgy5m3deaare52cdq

Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting

Feras Alasali, Khaled Nusair, Lina Alhmoud, Eyad Zarour
2021 Sustainability  
In order to minimize the impact of the pandemic on the performance of the forecasting model, a rolling stochastic Auto Regressive Integrated Moving Average with Exogenous (ARIMAX) model is developed in  ...  The proposed forecast model aims to improve the forecast performance by capturing the non-smooth demand nature through creating a number of future demand scenarios based on a probabilistic model.  ...  Acknowledgments: The authors are grateful to the engineering staff at the National Electric Power Grid Co (NEPCO) for supporting and collecting data which were used in this paper.  ... 
doi:10.3390/su13031435 fatcat:qkhpeniqqzd4teyylmj4en4ugm

Forecasting global fire emissions on sub‐seasonal to seasonal (S2S) timescales

Yang Chen, James T. Randerson, Shane R. Coffield, Efi Foufoula‐Georgiou, Padhraic Smyth, Casey A. Graff, Douglas C. Morton, Niels Andela, Guido R. Werf, Louis Giglio, Lesley E. Ott
2020 Journal of Advances in Modeling Earth Systems  
Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months.  ...  The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding  ...  , available at the NOAA state of the Ocean website (; and (3) The MERRA-2 climate data, available at NASA GES DISC (  ... 
doi:10.1029/2019ms001955 pmid:33042387 pmcid:PMC7540459 fatcat:7f2qhtxdlbg3nc2qaujgmudxri


2017 MATTER International Journal of Science and Technology  
The aim is to find the suitable methods and variables to be applicable to the situation similar to Singapore FIR and also to improve the forecasting accuracy.  ...  The conflicts and the density of air traffic in Singapore FIR are estimated in this paper by using the results of forecasting.  ...  ACKNOWLEDGEMENT This research was sponsored by the ATMRI of NTU and CAAS via ATMRI Project No. 2014-D2-ZHONG for Regional Airspace Capacity Enhancement -ASEAN Pilot.  ... 
doi:10.20319/mijst.2016.23.5569 fatcat:wzkub7u2rrcmjd7uww6g5vkrzq
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