Time series formalism : a systems approach
Name
1015190056-MIT.pdf
Description
Full printable version
Size
4.98 MB
Format
Adobe PDF
Checksum (MD5)
5c8b4667f112ef7e4c147e5c9aa87d66
Author(s)
Ayyaz, Usman
Advisor(s)
Devavrat Shah.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Time series data has become a modern day phenomena: from stock market data to social media information, modern day data exists as a continuous flow of information indexed by timestamps. Using this data to gather contextual inference and make future predictions is vital to gaining an analytical edge. While there are specialized time series databases and libraries available that optimize for performance and scale, there is an absence of a unifying framework that standardizes interaction with time series data sets. We introduce a python-based time series formalism which provides a SQL style querying interface alongside a rich selection of time series prediction algorithms. Users can forecast data or impute missing entries using a specialized prediction query which employs learning models under the hood. The decoupled architecture of our framework allows it to be easily substituted with any SQL database. We show the functionality of our abstraction with a single machine implementation which will be a building block towards a scalable distributed platform for time series analysis.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 51-52).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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