In this paper, we use integer programming (IP) to compute minimal forecast horizons for the classical dynamic lot-sizing problem (DLS). As a solution approach for computing forecast horizons, integer programming has been largely ignored by the research community. It is our belief that the...

Combinations of point forecasts from expert forecasters are known to frequently outperform individual forecasts. It is also well documented that combination by simple averaging very often has performance superior to that of more sophisticated combinations. This empirical fact is referred to as...

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...