Forecasting Long Haul Truckload Spot Market Rates

Abstract

The objective of this paper is to predict long haul truckload spot market rates for the near future. Short term spot rate forecasts help with making operational decisions, estimating budgets for shippers and cash flow for carriers. First, we check if the weekly spot rates time series is a Random Walk process. In which case a Naïve forecast is better than other auto-regressive time series models and thus we use it as our base forecast. We then use exogenous economic indicators as inputs to a Linear Regression model, fit using Elastic Net Regularization, to check if there are leading indicators for truckload spot rates. An important aspect of the truckload spot market is the periodic cycles of soft (decreasing market rates) and tight (increasing market rates) markets. Such changes in the time series, or concept drift, make old forecasting models irrelevant. We thus use two implicit and one explicit concept drift handling methods to retrain our forecasting models. We create forecasts for 1, 4, 8 and 12 weeks into the future and compare MAPEs of the models to conclude that Naïve model outperforms them in each case. We also discuss how explicit detection of concept drift provides useful information on changes in the market cycle for the stakeholders.