Recurring Partial Periodic Patterns in Time Series

A paper from The University of Tokyo proposes a model and algorithm for discovering recurring partial periodic patterns in time series. The work addresses the challenge of finding patterns that exhibit periodic behavior only during specific intervals, which can provide information about seasonal or temporal event associations. The author is R. Uday Kiran.

Use Cases

• Discovering seasonal event associations based on recurring patterns in time series.
• Identifying temporal intervals of periodic behavior based on the recurring pattern model.
• Developing efficient pattern mining algorithms based on the proposed Recurring Pattern growth and pruning technique.

Strengths

• The description provides a clear theoretical distinction between regular and recurring partial periodic patterns.
• The paper proposes a specific algorithm (Recurring Pattern growth) with an efficient pruning technique.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

The University of Tokyo

Collection Method

Proposed in an academic paper; the dataset likely contains experimental results.