Log-periodic oscillations naturally apearing before phase transitions in complex systems are investigated from the Warsaw Stock Exchange Index (WIG) in its 1991-2007 history. We find that crashes and rupture points on the market are better described by the log-divergent rather than power-law divergent amplitudes decorated with log-periodic behavior. However, the financial event predictions are not exact and depend on amount of data taken to make a fit. Contrary to this global analysis in long time period, another method based on the measurment of temporal fluctuations of the market is proposed. The method is directly related to local fractal dimension of financial time series and works exremely well for WIG data. We formulate the set of necessary conditions based on the local Hurst exponent behavior which have to be satisfied if the rupture point or crash point is coming. The current situation on the market, particularly related to the recent Fed intervention in September '07 is also discussed from the new method point of view.

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