dc.description.abstract | This NHH master thesis researches methodologies for forecasting a financial time series,
the Baltic Dry P1A spot price, one week and one month ahead. The methods researched
are four different strategies for time series prediction. The first is by fitting the future
timestep directly based on information about today. The second is a recursive strategy,
which iterates a one-step ahead prediction model. Third, a rectify implementation that
corrects bias from a recursive model, by training on the residuals. Last, a direct recursive
approach that fits each timestep directly with previous predictions as an added variable.
Our research finds that the Direct and Direct Recursive (DirRec) strategy is the most
accurate for both long and short forecast horizons. This performance is consistent when
testing on an independent test set. We cannot exclude that the Direct Recursive strategy
could perform better than Direct, as we get differing results when performing experiments
with fewer variables. An important trade-off is that the Direct Recursive strategy is
substantially more computationally heavy than the Direct strategy. | nb_NO |