Time-series clustering has been proven to provide effective information for further research. In contrast to the classic clustering, the time-series dataset comprises data changed with time.
This algorithm is denoted as "clustering seasonality patterns in the presence of errors"; the summarized algorithm is listed in the following figure. The major characteristic of this algorithm is to introduce a specific distance function and a dissimilarity function.
Please take a look at the R codes file ch_08_herror.R
from the bundle of R codes for previously mentioned algorithms. The codes can be tested with the following command:
> source("ch_08_herror.R")
Stock market is a complex dynamic system, and many factors can affect the market. For example, breaking financial news is also a key factor for this topic.
The characteristics of stock market data are large volume (near infinite), real time, high dimensions, and high complexity. The various data streams from stock markets go along with many events and correlations.
Stock market sentiment analysis is one topic related to this domain. The stock market is too complex with many factors that can affect the market. People's opinion or sentiment is one of the major factors.
The real-time information needs from the stock market require the fast, efficient online-mining algorithms. The time-series data accounts for various data including the stock market data that updates along with time.
To predict the stock market, the past data is really important. Using the past return on certain stock, the future price of that stock can be predicted based on the price-data stream.
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