Forecasting the red lentils commodity market price using SARIMA models

Abstract

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.

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

“…For the prediction of ocean temperature changes, we use the seasonal ARIMA time series model, which can effectively predict the overall seasonal temperature changes in the target sea area in the next 30 years [22][23][24][25][26][27], and then we can determine the future annual average temperature of the target sea area and compare it with the suitable ocean temperature for herring and mackerel; we can get the target migration position of the future fish school.…”

Monetary integration in South America: Elección of candidates through unsupervised machine learning

“…Finally, missing values for 2017 were forecasted using seasonal autoregressive integrated moving average (SARIMA) models. In addition to the SARIMA model accounting for seasonal effects, this model also has a stable layout and it is expressly designed for time series data (Divisekara, Jayasinghe, and Kumari, 2020).…”

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

“…Different approaches to agricultural commodity price forecasting have been tried, which can be divided into two major categories, namely univariate models and multivariate models. Univariate models use historical records of prices to make forecasts, such as Auto‐Regressive Integrated Moving Average models (ARIMA) (Jadhav et al, 2017; Pujiati et al, 2018) and its variants (Adanacioglu & Yercan, 2012; Divisekara et al, 2021; Li et al, 2012; Mithiya et al, 2019; Naidu et al, 2014), which are statistical modelling technique in commodity price forecasting credited with the ability to capture time series trends, and as such have mostly achieved relatively high levels of accuracy. However, ARIMA and its variants require large datasets and cannot capture the nonlinear relationship between historical and future records, machine learning methods like artificial neural networks (ANN) in commodity price forecasting come into play (Monge & Lazcano, 2022; Wang et al, 2019).…”