AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA

Currently the emergence of novel coronavirus (Sars-Cov-2), which causes COVID-19 pandemic and has become a serious health problem because high risk death. Therefore, fast appropriate action is needed to reduce spread pandemic. One way build prediction model so that it can be reference in taking steps overcome them. Because nature transmission this disease massive cause extreme data fluctuations between objects whose observational distances are far enough correlated with each other (long memory). The result determination best ARFIMA obtained predict additional recovering cases (1,0,489.0) an SMAPE value 12,44%, while case death (1.0.429.0) 13,52%. This shows accommodate well long memory effect, resulting small bias. Also estimating parameters, also simpler. For recovery death, number increasing even though still very compared recovery.