EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases

The time-series forecasting makes a substantial contribution in timely decision-making. In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to develop a forecasting model for nonstationary time series. The Phillips–Perron test (PPT) is used to define the nonstationarity of time series. EVDHM is applied over a time series to decompose it into respective subcomponents and reduce the nonstationarity. ARIMA-based model is designed to forecast the future values for each subcomponent. The forecast values of each subcomponent are added to get the final output values. The optimized value of ARIMA parameters for each subcomponent is obtained using a genetic algorithm (GA) for minimum values of Akaike information criterion (AIC). Model performance is evaluated by estimating the future values of daily new cases of the recent pandemic disease COVID-19 for India, USA, and Brazil. The high efficacy of the proposed method is convinced with the results.

[1]  Xu Li,et al.  Enhancing Localization Accuracy of MEMS-INS/GPS/In-Vehicle Sensors Integration During GPS Outages , 2018, IEEE Transactions on Instrumentation and Measurement.

[2]  F. Piazza,et al.  Analysis and forecast of COVID-19 spreading in China, Italy and France , 2020, Chaos, Solitons & Fractals.

[3]  Francisco Javier de Cos Juez,et al.  A hybrid ARIMA-SVM model for the study of the remaining useful life of aircraft engines , 2019, J. Comput. Appl. Math..

[4]  Diego Reforgiato Recupero,et al.  Deep learning and time series-to-image encoding for financial forecasting , 2020, IEEE/CAA Journal of Automatica Sinica.

[5]  H. Akaike.  A new look at the statistical model identification , 1974 .

[6]  Kuan-Yu Chen,et al.  A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan , 2007, Expert Syst. Appl..

[7]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[8]  Ram Bilas Pachori,et al.  Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals , 2018 .

[9]  Zeynep Ceylan.  Estimation of COVID-19 prevalence in Italy, Spain, and France , 2020, Science of The Total Environment.

[10]  Mehdi Khashei,et al.  Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) , 2009, Neurocomputing.

[11]  Hong Fan,et al.  Optimization Method for Forecasting Confirmed Cases of COVID-19 in China , 2020, Journal of clinical medicine.

[12]  Ilona Weinreich,et al.  Wavelet-based prediction of oil prices , 2005 .

[13]  Michael C. Wimberly,et al.  A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model , 2019, Environ. Model. Softw..

[14]  Sheng-Fu Liang,et al.  Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models , 2012, IEEE Transactions on Instrumentation and Measurement.

[15]  V. Ediger,et al.  ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[16]  Rajesh Kumar Tripathy,et al.  Multivariate Sliding-Mode Singular Spectrum Analysis for the Decomposition of Multisensor Time Series , 2020, IEEE Sensors Letters.

[17]  Ram Bilas Pachori,et al.  Eigenvalue Decomposition of Hankel Matrix-Based Time-Frequency Representation for Complex Signals , 2018, Circuits Syst. Signal Process..

[18]  Leandro dos Santos Coelho,et al.  Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil , 2020, Chaos, Solitons & Fractals.

[19]  Heng-Li Yang,et al.  An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting , 2016, Int. J. Artif. Intell. Tools.

[20]  T. Chakraborty,et al.  Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis , 2020, Chaos, Solitons & Fractals.

[21]  Hongwen He,et al.  ARIMA-Based Road Gradient and Vehicle Velocity Prediction for Hybrid Electric Vehicle Energy Management , 2019, IEEE Transactions on Vehicular Technology.

[22]  Guangzhi Wang,et al.  Leak detection for transport pipelines based on autoregressive modeling , 1993 .

[23]  U. Kumar,et al.  An Integrated SSA-ARIMA Approach to Make Multiple Day Ahead Forecasts for the Daily Maximum Ambient O3 Concentration , 2015 .

[24]  Ram Bilas Pachori,et al.  Electromyogram Signal Analysis Using Eigenvalue Decomposition of the Hankel Matrix , 2018, Advances in Intelligent Systems and Computing.

[25]  Chiou-Jye Huang,et al.  Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019 , 2020, Socio-Economic Planning Sciences.

[26]  Rajeev Sharma,et al.  Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition , 2018, Biomed. Signal Process. Control..

[27]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[28]  Ram Bilas Pachori,et al.  Joint Time-Frequency Domain-Based CAD Disease Sensing System Using ECG Signals , 2019, IEEE Sensors Journal.

[29]  Alejandro C. Frery,et al.  Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning , 2018, Acta tropica.

[30]  Héctor Pomares,et al.  Hybridization of intelligent techniques and ARIMA models for time series prediction , 2008, Fuzzy Sets Syst..

[31]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[32]  Chunshien Li,et al.  Complex Neurofuzzy ARIMA Forecasting—A New Approach Using Complex Fuzzy Sets , 2013, IEEE Transactions on Fuzzy Systems.