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Algorithms For Time Series Knowledge Mining
Fabian Moerchen
沈奕聰
Outline
• Introduction• Related work and motivation• Knowledge representation• Time series knowledge mining• Mining coincidence• Mining partial order
• Experiments• Discussion
Introduction
• Backgroud• Patterns mined from symbolic interval data can provide
explanation for the underlying temporal processes or anomalous behavior• Symbolic interval time series are an important data format for
discovering temporal knowledge• Numerical time series are often converted to symbolic interval
time series
Introduction
• Problems• Allen’s interval relations ‘s input usually consists of exact but
incomplete data and temporal constraints• Determining the consistency of the data• Answering queries about scenarios satisfying all constraints• Noisy and incorrect interval data
Introduction
• Propose• Time Series Knowledge Representation(TSKR)• Hierarchical language• Based on interval time series• Extends the Unification-based Temporal Grammar• Using itemset techniques
Related work and motivation
• Allen’s relations have severe disadvantages• Patterns from noisy interval data expressed with Allen’s interval relations are
not robust
Related work and motivation
• Allen’s relations have severe disadvantages• Patterns expressed with Allen’s interval relations are ambiguous
Related work and motivation
• Allen’s relations have severe disadvantages• Patterns expressed with Allen’s interval relations are not easily
comprehensible
Related work and motivation
• The TSKR extends these core ideas achieving higher robustness and expressivity• The hierarchical structure of the UTG• The separation of temporal concepts
Knowledge representationTones : basic primitives of the TSKR representing durationChord: a Chord pattern describes a time interval where k>0 Tones coincidePhrase: a paritial order of k>1 Chords
Time series knowledge mining——Mining coincidence
Time series knowledge mining——Mining coincidence
Time series knowledge mining——Mining coincidence
Time series knowledge mining——Mining partial order
Time series knowledge mining——Mining partial order
Experiments
Experiments
Experiments
Discussion
• Advantages• Hierarchical structure show the coinciding Tones and one Tone to show the
original numerical time series with the thresholds for discretization• The pruning by margin-closedness largely reduced the number of patterns • Effects on search space• Our novel data model conversion to itemset intervals greatly reduce the
redundancy• Search for phrases with our semantically motivated search space restrictions
are much faster than sequential pattern