This prediction can be strengthened or weakened by comparing

　　In addition, some cycles are defined by intrinsic characteristic properties of the system. The stock market performance curve China Mrl Hospital Elevator can be considered as a sum of the cyclical functions with different periods and amplitudes. It would be easy to analyze the repetition of typical patterns in stock market performance if they did not mask themselves. In other words, sometimes cycles overlap to form an abnormal extremum or offset to form a flat period. It is clear that a simple chart analysis has a certain limit in identifying and predicting the trend.Fortunately, mathematics is able to extract basic cycles so that historical quote curve can be decomposed into a set of sinus (or cosines) functions with different periods, amplitudes, and phases - that is something similar to a spectral analysis or a time series analysis. By selecting data with different historical periods, such spectral analysis can identify the major cycles, which have a dominant effect in a particular time frame.The recent research by Addaptron Software tools helped to detect for 40, 20, 10, and 5-year periods of ^GSPC the following major cycles (lines): 10, 8, 5, and 1.6 year.

Since each line has own amplitude it is easy to estimate the significance of timing analysis for overall investment performance. For example, an average annual return for the last 5 years approximated by linear function equals around 13% and the amplitude of cycle with 1.6-year period is equal 4.8%. Therefore, in case of 1.6-year cycle only, the return can be diminished to 13-4.8*2= 3.4% with the worst timing or it can be maximized to 13+4.8*2= 22.6% with the best timing.One of the techniques to build an extrapolation (forecasted curve) is to use the following two steps: (1) applying spectral (or time series) analysis to decompose the curve into basic functions, (2) composing these functions beyond the historical data. As a practical example by Addaptron software, the stock market prediction for the next five years on the basis of spectrum analysis: the stock market will suffer some volatility within the next several months but eventually it will go up until 2010-2011. Then it will crash in 2012-2013. (Charts are presented at "Research and Development"). Note, these years are approximate because the phase of cycles is fluctuating and, of course, something extraordinary can change the prediction picture.

This prediction can be strengthened or weakened by comparing with other forecasting techniques.To summarize, the direction of the overall market influences significantly an individual stock. There is no method that has been successfully enough to consistently beat the market. The same is applied to a simple combination of different methods. If all predictions fail, everybody scared, and nothing seems to work, one of the approaches to try is a time series prediction because it analyzes historical data and then builds prediction on the basis of changing performance "as is", objectively, without any pressure of emotion or external information.