To analyze the contribution of industry to a macroeconomic variable, GDP, is an important concern in economics. It plays major role in the government and industrial sector of Country. A time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable; recent developments in time series analysis suggest that more sophisticated time series models could provide more accurate benchmarks for economic models. In this paper the feature of yearly data on industrial contribution to GDP from fiscal year 1979-1980 to 2011-2012 are analyzed. There are two methods named Auto Regressive Integrated Moving Average (ARIMA) and Holt’s linear Smoothing method are fitted to our data set. In this study Box Jenkings is method were used to construct ARIMA model. The goodness of fit is determined via Akaike Information criterion and Bayesian information criterion. By comparing the measures of error we can see that ARIMA model is a better model than Holt’s linear trend model to forecast contribution of industry to GDP of Bangladesh