Abstract: This paper proposed a new method to estimate the missing data by using the filtering process. We used datasets without missing data and randomly missing data to evaluate the new method of estimation by using the Box Jenkins modeling technique to predict monthly average rainfall for site 5504035 Lahar Ikan Mati at Kepala Batas, P. Pinang station in Malaysia. The rainfall data was colle...

The purpose of this paper is to predict the lending interest rate and deposit Bangladesh using Autoregressive Integrated Moving Average (ARIMA) model by Box Jenkins. It has been found that ARIMA (1, 0, 1) appropriate in predicting both rates from 2022 2026 data presented World Bank Open Data 1976 2021. To test goodness fit, AIC (Akaike Information Criterion) BIC (Bayesian index values have calc...

We use the Autoregressive Integrated Moving Average (ARIMA) model and Facebook Prophet to predict closing stock price of Google during COVID-19 pandemic as well compare accuracy these two models’ predictions. first examine stationary dataset ARIMA(0,1,1) make predictions about pandemic, then we train using before January 1, 2021, after present. also a comparison prediction graphs models. The em...

OBJECTIVE Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. METHODS We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor reg...