Time series forecasting has proven useful in a number of different domains from weather to health monitoring. Sure you can easily over fit on the training data, but in general that's a data source/input problem where you need many high quality data sources to find the signal in the noise.

The world is chaotic sure, but there are still truths to be found in noisy time series data; saying that the world is too random to be knowable is a bit dismissive, no?