scholarly journals ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7952
Author(s):  
Ewa Chodakowska ◽  
Joanicjusz Nazarko ◽  
Łukasz Nazarko
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Moving Average ◽  
Arima Model ◽  
Load Forecasting ◽  
Model Identification ◽  
Electrical Load ◽  
Arima Models ◽  
Electrical Load Forecasting ◽  
The Impact

The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. In the study, an idealized ARIMA model obtained from real load data of the Polish power system was disturbed by noise of different levels. The model was then re-identified, its parameters were estimated, and new forecasts were calculated. The experiment allowed us to evaluate the robustness of ARIMA models to noise in their ability to predict electrical load time series. It could be concluded that the reaction of the ARIMA model to random disturbances of the modeled time series was relatively weak. The limiting noise level at which the forecasting ability of the model collapsed was determined. The results highlight the key role of the data preprocessing stage in data mining and learning. They contribute to more accurate decision making in an uncertain environment, help to shape energy policy, and have implications for the sustainability and reliability of power systems.

scholarly journals ARIMAX Model for Short-Term Electrical Load Forecasting

2019 ◽  
Vol 8 (4) ◽  
pp. 2786-2790
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Load Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Exogenous Variables ◽  
Time Series Modelling ◽  
Electrical Load Forecasting

The scope for ARIMAX approach to forecast short term load has gained a lot of significant importance.In this paper, ARIMAXmodel which is an extension of ARIMA model with exogenous variables is used for STLF on a time series data of Karnataka State Demand pattern. The forecasting accuracy of ARIMA model is enhanced by taking into consideration hour of the day and day of the week as exogenous variables for ARIMAX model. Forecasting performance is thus improved by considering these significant load dependent factors. The forecasted results indicate that the proposed model is more accurate according to mean absolute percentage error (MAPE) obtained during the testing period of the model. As the historical load data are available on the databases of the utility, researches in the areas of time series modelling are ongoing for electrical load forecasting. In the proposed paper real time demand data available on Karnataka Power Transmission Corporation Ltd. (KPTCL) website is taken to develop and test the proposedload forecasting model.The power utility system operational costs and its securitydepend on the load forecasting for next few hours. Regional load forecasting helps in the accurate management performance of generation of power plant. Today’s deregulated markets have great demand for prediction of electrical loads, required for generating companies. There has been tremendous growth in electric power demand and hence it is very much essentialfor the utility sectors to have theirdemand information in advance.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

Electrical Load Forecasting Using Time Series Model

2017 ◽  
Vol 19 (6) ◽  
pp. 3009-3018
Author(s):  
Jaeshin Park ◽  
◽  
Sungwan Bang
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Time Series Model ◽  
Electrical Load ◽  
Electrical Load Forecasting

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

scholarly journals Self-Identification ResNet-ARIMA Forecasting Model

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Model Identification ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Partial Autocorrelation Function

The challenging endeavor of a time series forecast model is to predict the future time series data accurately. Traditionally, the fundamental forecasting model in time series analysis is the autoregressive integrated moving average model or the ARIMA model requiring a model identification of a three-component vector which are the autoregressive order, the differencing order, and the moving average order before fitting coefficients of the model via the Box-Jenkins method. A model identification is analyzed via the sample autocorrelation function and the sample partial autocorrelation function which are effective tools for identifying the ARMA order but it is quite difficult for analysts. Even though a likelihood based-method is presented to automate this process by varying the ARIMA order and choosing the best one with the smallest criteria, such as Akaike information criterion. Nevertheless the obtained ARIMA model may not pass the residual diagnostic test. This paper presents the residual neural network model, called the self-identification ResNet-ARIMA order model to automatically learn the ARIMA order from known ARIMA time series data via sample autocorrelation function, the sample partial autocorrelation function and differencing time series images. In this work, the training time series data are randomly simulated and checked for stationary and invertibility properties before they are used. The result order from the model is used to generate and fit the ARIMA model by the Box-Jenkins method for predicting future values. The whole process of the forecasting time series algorithm is called the self-identification ResNet-ARIMA algorithm. The performance of the residual neural network model is evaluated by Precision, Recall and F1-score and is compared with the likelihood basedmethod and ResNET50. In addition, the performance of the forecasting time series algorithm is applied to the real world datasets to ensure the reliability by mean absolute percentage error, symmetric mean absolute percentage error, mean absolute error and root mean square error and this algorithm is confirmed with the residual diagnostic checks by the Ljung-Box test. From the experimental results, the new methodologies of this research outperforms other models in terms of identifying the order and predicting the future values.

scholarly journals Assessment of the Impact of COVID-19 pandemic on population level interest in Skincare: Evidence from a google trends-based Infodemiology study

2020 ◽  
Author(s):  
Hasan Symum ◽  
Md. F. Islam ◽  
Habsa K. Hiya ◽  
Kh M. Ali Sagor
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Population Level ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Search Volume ◽  
Quasi Experimental ◽  
Relative Search Volume ◽  
The Impact

AbstractBackgroundCOVID-19 pandemic created an unprecedented disruption of daily life including the pattern of skin related treatments in healthcare settings by issuing stay-at-home orders and newly coronaphobia around the world.ObjectiveThis study aimed to evaluate whether there are any significant changes in population interest for skincare during the COVID-19 pandemic.MethodsFor the skincare, weekly RSV data were extracted for worldwide and 23 counties between August 1, 2016, and August 31, 2020. Interrupted time-series analysis was conducted as the quasi-experimental approach to evaluate the longitudinal effects of COVID-19 skincare related search queries. For each country, autoregressive integrated moving average (ARIMA) model relative search volume (RSV) time series and then testing multiple periods simultaneously to examine the magnitude of the interruption. Multivariate linear regression was used to estimate the correlation between countries’ relative changes in RSV with COVID-19 confirmed cases/ per 10000 patients and lockdown measures.ResultsOut of 23 included countries in our study, 17 showed significantly increased (p<0.01) RSVs during the lockdown period compared with the ARIMA forecasted data. The highest percentage of increments occurs in May and June 2020 in most countries. There was also a significant correlation between lockdown measures and the number of COVID-19 cases with relatives changes in population interests for skincare.ConclusionUnderstanding the trend and changes in skincare public interest during COVID-19 may assist health authorities to promote accessible educational information and preventive initiatives regarding skin problems.

COVID-19 ARIMA Model Forecast: Confirmed, Mortality & Recovered Cases in 13 Caribbean Countries (Preprint)

2020 ◽  
Author(s):  
Debjyoti Talukdar ◽  
Dr. Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Local Health ◽  
Health Administration ◽  
Model Forecast ◽  
Time Period ◽  
The Impact

BACKGROUND Rapid spread of SARS nCoV-2 virus in Caribbean region has prompted heightened surveillance with more than 350,000 COVID-19 confirmed cases in 13 Caribbean countries namely Antigua and Barbados, Bahamas, Barbados, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago. OBJECTIVE The aim of our study is to analyze the impact of coronavirus (SARS nCoV-2) in 13 Caribbean countries in terms of confirmed cases, number of deaths and recovered cases. Current and projected forecasts using advanced autoregressive integrated moving average (ARIMA) models will enable local health organisations to plan future courses of action in terms of lockdown and managing essential public services. METHODS The study uses the auto regressive integrated moving average (ARIMA) model based upon time series pattern as per data retrieved from John Hopkins University, freely accessible on public domain and used for research and academic purposes. The data was analyzed using STATA 14 SE software between the time period - Jan 22, 2020 till May 27, 2020 using ARIMA time series analysis. It involves generalizing an autoregressive moving average model to better understand the data and predict future points in the time series until June 15, 2020. RESULTS The results show the predicted trend in terms of COVID-19 confirmed, mortality and recovered cases for 13 Caribbean countries. The projected ARIMA model forecast for the time period - May 25, 2020 to May 31, 2020 show 20278 (95% CI 19433.21 - 21123.08) confirmed cases, 631 (95% CI 615.90 - 646.51) deaths and 11501 (95% CI 10912.45 - 12089) recovered cases related to SARS nCoV-2 virus. The final ARIMA model chosen for confirmed COVID-19 cases, number of deaths and recovered cases are ARIMA (4,2,2), ARIMA (2,1,2) and ARIMA (4,1,2) respectively. All chosen models were compared with other models in terms of various factors like AIC/BIC (Akaike Information Criterion/Bayesian Information Criterion), log likelihood, p-value significance, coefficient < 1 and 5% significance. The autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs were plotted to reduce bias and select the best fitting model. CONCLUSIONS As per the results of the forecasted COVID-19 models, there is a steady rise in terms of confirmed, recovered and mortality cases during the time period March 1, 2020 until May 27, 2020. It shows an increasing trend for confirmed and recovered COVID-19 cases and slowing of the number of mortality cases over a period of time. The predicted model will help the local health administration to devise public policies in terms of awareness measures, lockdown and essential health services accordingly.

scholarly journals A hybrid of artificial fneural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting

2021 ◽  
Vol 16 (1) ◽  
pp. 25-35
Author(s):  
Samir K. Safi ◽  
Olajide Idris Sanusi
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Time Series Forecasting ◽  
Arima Models ◽  
Artificial Neural Network Ann ◽  
Novel Coronavirus ◽  
Two Stages ◽  
Second Wave

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

scholarly journals Forecasting the Prevalence of COVID-19 in Maharashtra, Delhi, Kerala, and India using an ARIMA model

2020 ◽  
Author(s):  
Strong P Marbaniang
Keyword(s):  
Time Series ◽  
Performance Measures ◽  
Arima Model ◽  
Arima Models ◽  
Wuhan City ◽  
Policy Interventions ◽  
Time Series Analyses ◽  
The Government ◽  
The Impact ◽  
Best Fit

Abstract Aims: As the whole world was preparing to welcome the year 2020, a new deadly virus, COVID-19, was reported in the Wuhan city of China in late December 2019. By May 18, 2020, approximately 4.7 million cases and 0.32 million deaths had been reported globally. There is an urgent need to predict the COVID-19 prevalence to control the spread of the virus.Methods: Time-series analyses can help understand the impact of the COVID-19 epidemic and take appropriate measures to curb the spread of the disease. In this study, an ARIMA model was developed to predict the trend of COVID-19 prevalence in the states of Maharashtra, Delhi and Kerala, and India as a whole.Results: The prevalence of COVID-19 from 16 March 2020 to 17 May 2020 was collected from the website of Covid19india. Several ARIMA models were generated along with the performance measures. ARIMA (2,3,1), ARIMA (2,2,0), ARIMA (2,2,0), and ARIMA (1,3,1) with the lowest MAPE (5.430, 10.440, 2.607, and 2.390) for Maharashtra, Delhi, Kerala, and India were selected as the best fit models respectively. The findings show that over the next 20 days, the total number of confirmed COVID-19 cases may increase to 2.45 lakhs in India, 93,709 in Maharashtra, 19,847 in Delhi, and 925 in Kerala.Conclusion: The results of this study can throw light on the intensity of the epidemic in the future and will help the government administrations in Maharashtra, Delhi, and Kerala to formulate effective measures and policy interventions to curb the virus in the coming days.

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