1. A computer-implemented method for providing at least one parameter for use with a forecast model, the method being executed using one or more processors and comprising: receiving, by the one or more processors, a first context vector, the first context vector comprising a plurality of context attributes at a current time, that describe a first context of an energy time series; retrieving, by the one or more processors, a first parameter vector from a repository based on the first context vector, the repository electronically storing a plurality of parameter vectors that describe respective contexts, each of the respective contexts comprising a plurality of external factors defining one or more of a seasonal pattern, an event and an exception and each of the plurality of parameter vectors comprising one or more parameters; parameterizing, by the one or more processors, the forecast model based on the one or more parameters provided in the first parameter vector to provide a parameterized forecast model; optimizing, by the one or more processors, the parameterized forecast model using a local optimization that runs in parallel to a global optimization to provide an optimized forecast model based on the first context; forecasting, by the one or more processors, one or more values using the optimized forecast model; and adjusting, by the one or more processors, an energy supply based on the one or more values.
1. A computer-implemented method for providing at least one parameter for use with a forecast model, the method being executed using one or more processors and comprising: receiving, by the one or more processors, a first context vector, the first context vector comprising a plurality of context attributes at a current time, that describe a first context of an energy time series; retrieving, by the one or more processors, a first parameter vector from a repository based on the first context vector, the repository electronically storing a plurality of parameter vectors that describe respective contexts, each of the respective contexts comprising a plurality of external factors defining one or more of a seasonal pattern, an event and an exception and each of the plurality of parameter vectors comprising one or more parameters; parameterizing, by the one or more processors, the forecast model based on the one or more parameters provided in the first parameter vector to provide a parameterized forecast model; optimizing, by the one or more processors, the parameterized forecast model using a local optimization that runs in parallel to a global optimization to provide an optimized forecast model based on the first context; forecasting, by the one or more processors, one or more values using the optimized forecast model; and adjusting, by the one or more processors, an energy supply based on the one or more values. 5. The method of claim 1 , wherein the repository is organized into a binary search tree.