Abstract:
"
Forecasting by definition (Oxford languages, 2019) revolves around the ability to predict the
future occurrence of an event or a trend. This has gained immense popularity across various
industries as they enable firms plan between priorities and allocate scares resources within
initiatives to capitalize on the opportunity and help improve the firm’s profitability and investor
confidence. The data presented through the forecasting model, would trigger a series of planning
actions which could lead towards either importing more of product or towards holding back the
supply to market to stabilize the price at which its traded. Coffee imports to Sri Lanka is similar
in nature and is constantly exposed to high volatility. The import prices were forecasted by using
the time series data of monthly average prices for the period of 9 years (January 2013 to
December 2020). The ARIMA model introduced by Box and Jenkins (1970) which is the most
widely used amongst time series models, whilst the same was constructed using Exponential
Smoothing. Both outcomes were used for predictions of the future 5 years and R2, RMSE,
MAPE, MAE and normalized BIC parameters were used to test the reliability of model. The
outcome generated were useful for the organization in decision making as it demonstrates the
range in which the future price can lead to, whilst also allowing the management scenario plan
with a regression model to understand at which price it needs to sell its product to maximize
profits. The model can be further improved for the decision-making process through additional
timeseries extensions and through the reduction of data noise. "
