This paper discusses the application of Neural Logic Networks in time series forecasting. Neural Logic Networks are systems that are developed to incorporate the strengths of neural networks and expert systems, which is equivalent to the human processes of logic and intuition [1]. This paper examines their prospect in forecasting of time series and compares their performance with linear models and the Feed Forward Neural Network. Additionally, the suitability of logic rules, generated from a Neural Logic Network, as potential inputs to forecasting systems is also examined. They are applied on two different meteorological series with strong features: a mean hourly wind speed series that exhibits behavior similar to random walk and an hourly solar radiation series selected because of its seasonal nátuře with discontinuities.