Machine learning algorithms for time series in financial markets

Algorithms for Segmenting Time Series

As with most computer science problems, representation of the data is the key to ecient and eective solutions. Piecewise linear representation has been used for the representation of the data. This representation has been used by various researchers to support clustering, classication, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain...

Machine Learning Applications in Financial Markets

2 Acknowledgments I would like to thank my guide, Professor Saketh Nath for his constant help, support and guidance. Abstract Stock market prediction with data mining techniques is one of the most important issues to be investigated. We intend to present a system that predicts the changes of stock trend by analyzing the influence of news articles.

Financial time series forecasting with machine learning techniques: a survey

Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques emplo...

Financial Time Series Forecasting – a Machine Learning Approach

The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one period and declining in the next. Stock traders make money from buying equity when they are at their lowest and selling when they are at their highest. The logical question would be: "What Causes Stock Prices To Change?". At the most fundamental level, the answer to this would be the dema...

FINANCIAL TIME SERIES PREDICTION USING MACHINE LEARNING ALGORITHMS by LESLIE TIONG

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...