  Decision-focused learning has emerged as a promising approach for decision
making under uncertainty by training the upstream predictive aspect of the
pipeline with respect to the quality of the downstream decisions. Most existing
work has focused on single stage problems. Many real-world decision problems
are more appropriately modelled using multistage optimisation as contextual
information such as prices or demand is revealed over time and decisions now
have a bearing on future decisions. We propose decision-focused forecasting, a
multiple-implicitlayer model which in its training accounts for the
intertemporal decision effects of forecasts using differentiable optimisation.
The recursive model reflects a fully differentiable multistage optimisation
approach. We present an analysis of the gradients produced by this model
showing the adjustments made to account for the state-path caused by
forecasting. We demonstrate an application of the model to an energy storage
arbitrage task and report that our model outperforms existing approaches.
