Canada is the world's largest producer of lentils, accounting for 32.8% of total production in the world. However, the production of lentils are prone to fluctuate due to the impact of erratic factors such as weather conditions and economic crises. Consequently, the price of the commodity will be changed and volatile. Therefore, the approach of modeling and forecasting future price based on the preceding data will provide representative figures to make decisions regarding the lentil production for growers and end users. Hence, the objective of this study is to model and forecast the red lentil prices using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). Eight years of weekly data starting from 2010 to 2019 which comprise 521 observations, obtained from Saskatchewan.ca were used in this study. The average red lentil price in Saskatchewan was dollar 24.75 per 100 lb, and weekly prices were highly fluctuating over time. The seasonality and volatility of red lentils are modeled and forecasted by calculating the seasonal index and applying SARIMA models to the time series. The results reveal that the SARIMA (2,1,2) (0,1,1)[52] model provides the best in sample and out-sample performance when predicting the red lentil prices. Hence, this model can be utilized by both growers and end users in making optimal production decisions and in managing overall price risk.

One of the easiest and fastest ways of building a healthy financial future is investing in the global market. However, the prices of the global market are highly volatile due to the impact of economic crises. Therefore, future prediction and comparison lead traders to make the low-risk decisions with price. The present study is based on time series modelling to forecast the daily close price values of financial instruments in the global market. The forecasting models were tested with two sample sizes, namely, 5-year close price values for correlation analysis and 3-year close price values for model building from 2013 January to 2018 January. The forecasting capabilities were compared for both ARIMA and GARCH class models, namely, TGARCH, APARCH, and EGARCH. The best-fitting model was selected based on the minimum value of the Akaike information criterion (AIC) and Bayesian information criteria (BIC). Finally, the comparison was carried out between ARIMA and GARCH class models using the measurement of forecast errors, based on the Root Mean Square Deviation (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE). The GARCH model was the best-fitted model for Australian Dollar, Feeder cattle, and Coffee. The APARCH model provides the best out-of-sample performance for Corn and Crude Oil. EGARCH and TGARCH were the better-fitted models for Gold and Treasury bond, respectively. GARCH class models were selected as the better models for forecasting than the ARIMA model for daily close price values in global financial market instruments.