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We introduce twenty four two-parameter families of advanced time seriesforecasting functions using a new and nonparametric approach. We also introducethe concept of powering and derive nonseasonal and seasonal models withexamples in education, sales, finance and economy. We compare the performanceof our twenty four models to both Holt--Winters and ARIMA models for bothnonseasonal and seasonal times series. We show in particular that our modelsnot only do not require a decomposition of a seasonal time series into trend,seasonal and random components, but leads also to substantially lower sum ofabsolute error and a higher number of closer forecasts than both Holt--Wintersand ARIMA models. Finally, we apply and compare the performance of our twentyfour models using five-year stock market data of 467 companies of the S&P500.

Variations on two-parameter families of forecasting functions:  seasonal/nonseasonal Models, comparison to the exponential smoothing and  ARIMA models, and applications to stock market data