Enhancing DWT for Recent-Biased Dimension Reduction of Time Series Data
نویسندگان
چکیده
In many applications, old data in time series become less important as time elapses, which is a big challenge to traditional techniques for dimension reduction. To improve Discrete Wavelet Transform (DWT) for effective dimension reduction in this kind of applications, a new method, largest-latest-DWT, is designed by keeping the largest k coefficients out of the latest w coefficients at each level of DWT transform. Its efficiency and effectiveness is demonstrated by our experiments.
منابع مشابه
A Recent-Biased Dimension Reduction Technique for Time Series Data
There are many techniques developed for tackling time series and most of them consider every part of a sequence equally. In many applications, however, recent data can often be much more interesting and significant than old data. This paper defines new recent-biased measures for distance and energy, and proposes a recent-biased technique based on DWT for time series in which more recent data ar...
متن کاملA Dimension-Reduction Framework for Human Behavioral Time Series Data
Human-machine interaction has become one of the most active research areas, and influenced several new paradigms of computing such as Social computing,Mobile computing, and Pervasive/Ubiquitous computing, which are typically concerned with the study of human user’s behavior to facilitate behavioral modeling and prediction. Human behavioral data are usually high-dimensional time series, which ne...
متن کاملFeature Extraction Methods for Time Series Data in SAS Enterprise MinerTM
Because time series data have a unique data structure, it is not easy to apply some existing data mining tools directly to the data. For example, in classification and clustering problems, each time point is often considered a variable and each time series is considered an observation. As the time dimension increases, the number of variables also increases, in proportion to the time dimension. ...
متن کاملRiver Discharge Time Series Prediction by Chaos Theory
The application of chaos theory in hydrology has been gaining considerable interest in recent years.Based on the chaos theory, the random seemingly series can be attributed to deterministic rules. Thedynamic structures of the seemingly complex processes, such as river flow variations, might be betterunderstood using nonlinear deterministic chaotic models than the stochastic ones. In this paper,...
متن کاملModel Based Method for Determining the Minimum Embedding Dimension from Solar Activity Chaotic Time Series
Predicting future behavior of chaotic time series system is a challenging area in the literature of nonlinear systems. The prediction's accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. On the other hand the cyclic solar activity as one of the natural chaotic systems has significant effects on earth, climate, satellites and space missions. Several m...
متن کامل