Time series forecasting plays an important role in financial activities since it allows investors to make better investment choices and reduce investment risk. Financial institutions forecasts rely essentially on algorithms based on statistics and probability theory. In this paper we explore an approach to forecasting based on information theory. Our approach is based on the intuition that a strong relationship exists between predictability and compressibility: if a string of data can be conveniently compressed it is because some kind of regularity or law has been detected that can be exploited to build a more compact encoding of the information contained in the string. Conversely, a data string generated at random cannot be further compressed because no regularity can be found on it. Information theory and Kolmogorov theory have been used as the foundation for the analysis and the development of compression algorithms. Interestingly, time series predictability also depends on ability to find recurring patterns on past data. The close relationship between compressibility and predictabiliiy has been recently addressed by Feder and Gutman which describe an algorithm for the prediction of binary sequences that they demonstrated to be asymptotically optimal independently of the statistical distribution of data. This is an interesting result that can be conveniently applied to time series forecasting. This working paper describes the forecasting algorithm and discusses issues related to its adaptation for time series forecasting. We also provide some preliminary results.