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...

We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting...

The aim of our model is to leverage recurrent neural networks to predict the trends of stocks traded on India’s National Stock Exchange. By integrating an analysis of the stock’s historic price, and the contemporary market sentiment of the parent company, we endeavored to build a model that...