Forecasting monthly world tuna prices with a plausible approach

Abstract

Skipjack, the most caught species of tuna globally, is a critical raw material for tuna industry in Thailand, the world’s largest tuna-processing hub. However, tuna processors are finding it difficult to manage costs of these imported materials because of price fluctuations over time. Whereas most time series forecasting methods used in the literature model only three components: trend, seasonality and error, this study proposes a method to handle a fourth component as well: cycle. This method smooths monthly price data using a cubic spline that can detect cycles varying in both frequency and amplitude, and thus generates plausible forecasts by refitting the model after duplicating data from its most recent cycle. Results show that world tuna prices have a slightly upward trend in cyclical patterns with each cycle lasting approximately six years. Peak-to-peak amplitudes suggest that prices reached their peak at 2,350 US dollars per metric ton in 2017 and have started to fall, but will rebound after 2021.

Keywords

• skipjack tuna prices
• seasonal adjustment
• cyclical pattern
• spline interpolation
• time series forecasting