Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Data Mining and Knowledge Discovery
MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Querying continuous functions in a database system
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Simultaneous Equation Systems for Query Processing on Continuous-Time Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient in-database maintenance of ARIMA models
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
F2DB: The Flash-Forward Database System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
Towards the automated extraction of flexibilities from electricity time series
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Research challenges for energy data management (panel)
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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We demonstrate TimeTravel, an efficient DBMS system for seamless integrated querying of past and (forecasted) future values of time series, allowing the user to view past and future values as one joint time series. This functionality is important for advanced application domain like energy. The main idea is to compactly represent time series as models. By using models, the TimeTravel system answers queries approximately on past and future data with error guarantees (absolute error and confidence) one order of magnitude faster than when accessing the time series directly. In addition, it efficiently supports exact historical queries by only accessing relevant portions of the time series. This is unlike existing approaches, which access the entire time series to exactly answer the query. To realize this system, we propose a novel hierarchical model index structure. As real-world time series usually exhibits seasonal behavior, models in this index incorporate seasonality. To construct a hierarchical model index, the user specifies seasonality period, error guarantees levels, and a statistical forecast method. As time proceeds, the system incrementally updates the index and utilizes it to answer approximate and exact queries. TimeTravel is implemented into PostgreSQL, thus achieving complete user transparency at the query level. In the demo, we show the easy building of a hierarchical model index for a real-world time series and the effect of varying the error guarantees on the speed up of approximate and exact queries.