Exchange rates are a significant reflector of the well-being of an economy of importance to investors, government and also policy makers. Exchange rates have however shown significant fluctuation and volatility depicting high degree of unpredictability. This study is aimed to develop a tool for predicting the future exchange rate movements with the application to Kenyan shilling against the United states dollar exchange rate data observed from August 2005 to August 2016. The data was obtained from the Central bank of Kenya Forex statistics. Time series models are one of the most popular and explanatory models with regard to exchange rate data, especially autoregressive integrated moving average (ARIMA) models. The study utilized daily exchange rate data to come up with a suitable ARIMA model that fits the time series data as well as provide adequate daily Ksh/USD exchange rate forecast. The study intends to utilize statistical techniques through the use of some R-programming language packages related in the analysis of time series data. From the analysis and model formulation process, different candidate models will be proposed. The relatively best model will be selected from the proposed models relying on results from goodness of fit measures. These measures to be used by this study include Akaike information criterion (AIC), Bayesian information criterion (BIC) and the mean error measure (ME).