1. A computer-implemented method for time series analysis, comprising: receiving time series data in a computer system; differentiating, by a processor subsystem of the computer system, the time series data 0, 1 and 2 times to generate three transformed time series; differentiating by the processor subsystem each transformed time series according to seasonal cycles 0, 1 and 2 times; determining, by the processor subsystem, a variance for each seasonally transformed time series; selecting one or more subsets of variances among the determined variances; identifying a trend behavior among each selected subset of variances; determining seasonality of the time series data by providing a frequency spectrum of the time series data; processing the frequency spectrum to create a processed frequency spectrum by: filtering the frequency spectrum for reducing noise of the data, truncating the frequency spectrum at low frequencies, and weighting high frequency contributions over low frequency contributions of the frequency spectrum; and extracting a periodic cycle based on the processed frequency spectrum; building combinations of trend types and seasonality types, the trend types including non-trend, additive trend and multiplicative trend, and the seasonality types including non-trend seasonality, additive seasonality and multiplicative seasonality; and selecting, by the processor subsystem, an appropriate combination of trend type and seasonality type of the time series data among the built combinations, based on the identified trend behaviors in the subsets of variances.