Predictive Mining of Time Series Data

✍ Scribed by Java A., Perlman E.

Tongue: English
Leaves: 11
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

All-sky monitors are a relatively new development in astronomy, and their data represent a largely untapped resource. Proper utilization of this resource could lead to important discoveries not only in the physics of variable objects, but in how one observes such objects. We discuss the development of a Java toolbox for astronomical time series data. Rather than using methods conventional in astronomy (e.g., power spectrum and cross-correlation analysis) we employ rule discovery techniques commonly used in analyzing stock-market data. By clustering patterns found within the data, rule discovery allows one to build predictive models, allowing one to forecast when a given event might occur or whether the occurrence of one event will trigger a second. We have tested the toolbox and accompanying display tool on datasets (representing several classes of objects) from the RXTE All Sky Monitor. We use these datasets to illustrate the methods and functionality of the toolbox. We have found predictive patterns in several ASM datasets. We also discuss problems faced in the development process, particularly the difficulties of dealing with discretized and irregularly sampled data. A possible application would be in scheduling target of opportunity observations where the astronomer wants to observe an object when a certain event or series of events occurs. By combining such a toolbox with an automatic, Java query tool which regularly gathers data on objects of interest, the astronomer or telescope operator could use the real-time datastream to efficiently predict the occurrence of (for example) a flare or other event.By combining the toolbox with various preprocessing and dimensionality reduction tools, one could predict events which may happen on variable time scales.

📜 SIMILAR VOLUMES

Predictive Mining of Time Series DatPred

📁 Predictive Mining of Time Series DatPredictive Mining of Time Series Data

✍ Java A., Perlman E. 📂 Library 🌐 English

Data Mining for Prediction. Financial Se

📁 Data Mining for Prediction. Financial Series Case

✍ Zemke S. 📂 Library 📅 2001 🌐 English

Hard problems force innovative approaches and attention to detail, their exploration often contributing beyond the area initially attempted. This thesis investigates the data mining process resulting in a predictor for numerical series. The series experimented with come from financial data - usually

Data mining in time series databases

📁 Data mining in time series databases

✍ Mark Last, Abraham Kandel, Horst Bunke 📂 Library 📅 2004 🏛 World Scientific 🌐 English

This thin book presents eight academic papers discussing handling of sequences. I did not find any of them interesting on its own or good as a survey, but academics doing research in machine learning may disagree. If you are one, you most likely can get the original papers. If you are a practitioner

Data Mining In Time Series Databases

📁 Data Mining In Time Series Databases

✍ Mark Last, Abraham Kandel, Horst Bunke 📂 Library 📅 2004 🏛 World Scientific 🌐 English

Adding the time dimension to real-world databases produces TimeSeries Databases (TSDB) and introduces new aspects and difficultiesto data mining and knowledge discovery. This book covers thestate-of-the-art methodology for mining time series databases. Thenovel data mining methods presented in the b

Data Mining In Time Series Databases

📁 Data Mining In Time Series Databases

✍ Mark Last, Abraham Kandel, Horst Bunke 📂 Library 📅 2004 🏛 World Scientific 🌐 English

Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in t

Predictive Data Mining Models

📁 Predictive Data Mining Models

✍ David L. Olson, Desheng Wu 📂 Library 📅 2020 🏛 Springer Singapore 🌐 English

<p>This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in