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COVID-19 is still a major pandemic threatening all the world. In Palestine, there were 26,764 COVID-19 cumulative confirmed cases as of 27th August 2020. In this paper, two statistical approaches, autoregressive integrated moving average (ARIMA) and k-th moving averages - ARIMA models are used for modeling the COVID-19 cumulative confirmed cases in Palestine. The data was taken from World Health Organization (WHO) website for one hundred seventy-six (176) days, from March 5, 2020 through August 27, 2020. We identified the best models for the above mentioned approaches that are ARIMA (1,2,4) and 5-th Exponential Weighted Moving Average – ARIMA (2,2,3). Consequently, we recommended to use the 5-th Exponential Weighted Moving Average – ARIMA (2,2,3) model in order to forecast new values of the daily cumulative confirmed cases in Palestine. The forecast values are alarming, and giving the Palestinian government a good picture about the next number of COVID-19 cumulative confirmed cases to review her activities and interventions and to provide some robust structures and measures to avoid these challenges.

In this article, we discuss finding the optimal k of (i) kth simple moving average, (ii) kth weighted moving average, and (iii) kth exponential weighted moving average based on simulated autoregressive AR(p) model. We run a simulation using the three above examining method under specific conditions. The main finding is that the optimal k = 4 and then k = 3. Especially, the fourth WMA ARIMA model, fourth EWMA ARIMA model, and third EWMA ARIMA model are the best forecasting models among others, respectively. For all the six real data reveal the similar results of simulation study. © 2017 Taylor & Francis Group, LLC.

In this article, we discuss finding the optimal k of (i) kth simple moving average, (ii) kth weighted moving average, and (iii) kth exponential weighted moving average based on simulated ARIMA(p, d, q) model. We run a simulation using the three above examining methods under specific conditions. The main finding is that 5th exponential weighted moving average (5th EWMA) ARIMA model is the best forecasting model among others, which means the optimal k = 5. For Turkish Telecommunications (TTKOM) stock market, real data reveal the similar results of simulation study. © 2017 Taylor & Francis Group, LLC.

In this paper, we discuss three analytical time series models for selecting the more effective with an accurate forecasting models, among others. We analytically modify the stochastic realization utilizing (i) k-th moving average,(ii) k-th weighted moving average, and (iii) k-th exponential weighted moving average processes. The examining methods have been applied for 1000 independent datasets for five different parameters with possible orders pq 5+≤. We consider stationary data () 0 d=, and non-stationary data with first and second differences () 1, 2 d= for ARIMA models. We consider short term () 50 n= and long term,() 500 n= observations. A similar forecasting models was developed and evaluated for the daily closing price of Stock Price of the PALTEL company in Palestine. The main finding is that, in most simulated datasets one or more of the proposed models give better forecast accuracy than the classical model (ARIMA). Specially, in most simulated datasets 3–time Exponential Weighted Moving Average based on Autoregressive Integrated Moving Average (EWMA3-ARIMA) is the best forecasting model among all other models. For PALTEL Stock Price, the best forecasting model is 3–time Moving Average based on Autoregressive Integrated Moving Average (MA3-ARIMA) among all other models.