Salmon price forecasting : a comparison of univariate and multivariate forecasting methods

Abstract

The salmon industry is becoming an intrinsic part of the Norwegian economy. It is a commercial activity revolving mostly around a single homogenous product. Consequently, salmon farmers and other participants along the value chain can gain substantial insight into how to conduct their business by understanding future spot price movements, primarily since salmon exhibits considerable price volatility. Therefore, it is of great interest to investigate the extent to which time series forecasting can support short- and long-term strategic planning 12 months ahead. Previous research, such as A.G Guttormsen (1999), has shown promising results from applying well known univariate methods. However, most of the studies are outdated, given market changes. Subsequently, this study will focus on partly proven univariate forecasting methods and two multivariate methods regarding Atlantic salmon price forecasting compared to each other and simple benchmarks. The univariate methods are ARIMA and ETS, while the regression methods applied are GAM and LASSO. We chose GAM and LASSO to allow for non-parametric and parametric fit, respectively. The univariate models utilized the spot price of Atlantic salmon, while the multivariate models are supplemented with 20 variables. Each method's accuracy is assessed using mean absolute error and root mean square error for more straightforward interpretability. Results show that univariate ARIMA and benchmark naïve with an STL decomposition outperform GAM and LASSO, suggesting simpler models are perhaps preferable. GAM is superior among the multivariate methods, which can possibly be attributed to it allowing for non-linear relationships. Despite the poor performance, the multivariate models indicate the importance of several variables. Although the models do not provide satisfactory results, it unfolds the possibility of further research using other regression approaches on Atlantic salmon spot price forecasting.