Comparing time series and associative classification approaches to quantitative stock trading. Can time dimension become negligible in time series forecasting? This work tries to answer that question in a specific, still interesting, domain: economic quantitative analysis. The study compares a new, time-independent methodology, built upon data mining and association rules extraction, to a set of classical, time-dependent approaches for time series analysis. Forecasting methods have been used to build a trading system that impersonates a daily trader operating on Italian stock exchange market. Models performances have been compared in term of profit and accuracy. Results show that time-independent approach is comparable to time-dependent methods.