#Stock charting software fft series
As a typical time series, not only have stock time series the features of time series, but also the trend of stock prices is directly related to the people’s vital interests.
Because of the two features, all kinds of time series forecasting have become a present hot research, one of which is the prediction of stock time series, stock prediction for short. That is to say, some special time subseries will repeat in the entire time series. The influence can be described by time series’ period, nonstationarity, varying volatility, and so on. That is, the historical values of observations will exert an influence on the future values in the time series. (a) The historical information will affect the future trend. Researches show that the time series have two import features. It is the most commonly encountered data type, touching almost every aspect of human life, for example, the meteorological time series, the time series of stock prices (stock time series for short) which are composed of stock price observations, and the time series of personal health that are consisted of the observation of blood pressure, temperature, white corpuscle, and so forth. IntroductionĪ time series is a series of observations listed in time order. Experimental results show that the predictive power of a pattern varies a great deal for different shapes and each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. However, there are some disputes on whether the K-line patterns have predictive power in academia.
Stock price prediction based on K-line patterns is the essence of candlestick technical analysis.
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