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The Coronavirus Disease 2019 (COVID-19) pandemic which has claimed thousands of lives over the past 6 months and counting, has resulted in great panic by many worldwide, as the number of death cases is now monotonically growing by the day. This paper isaimed at developing a suitable Autoregressive integrated moving average (ARIMA) model, which shall be used to fit a most appropriate model for the daily number of total deaths recorded in ECOWAS that are traceable to the COVID-19 pandemic. We discovered that ARIMA (2, 1, 0) was the most appropriate model for forecasting the number of COVID-19 related deaths recorded in ECOWAS. A forecast of 234 days from 11th May, 2020 to 31th December, 2020, was carried out and we discovered that, the COVID-19 mortality in ECOWAS may likely take a daily upward trend for the next 6 months.

nd June 2016. It is believed that this intervention is the current Nigerian economic recession which is bedevilling the country. This paper is aimed at proposing an intervention model to explain this phenomenon. The method to adopt is the autoregressive integrated moving average (ARIMA) method. The pre-intervention exchange rates are non-stationary. Differencing it once makes it stationary. Its partial autocorrelation and autocorrelation structures suggest a random noise fit. The difference between post-intervention forecasts and observations is modelled for the intervention transfer function. This is observed to be statistically significant and the forecasts based on it are very closely in agreement with the corresponding observations.

Time series analysis of Nigerian Consumer Price Index (NCPI) Data is done. It is observed that it is seasonal. A multiplicative seasonal autoregressive integrated moving average (ARIMA) model , (0, 1, 1)x(0, 1, 1) 12 , is fitted to the series. A visual inspection of the actual and the fitted time plots reveals a close agreement between the two.