Information-Systems department Literature survey topic: Time Series Analysis Course: Data-Mining Instructor: Dr. Mark Last
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Title: Information-Systems Department Literature Survey on Time Series Analysis

Section 1: Introduction
Description 1: Introduce the topic of time series analysis and its relevance to the Information-Systems department. Mention the importance of data mining in this context.

Section 2: Knowledge Discovery in Time Series Databases (TSDB)
Description 2: Discuss the process of knowledge discovery in TSDB, including data cleaning, filtering, identifying key attributes, and extracting association rules.

Section 3: Stage-1: Pre-processing of TSDB
Description 3: Describe the steps involved in pre-processing time series databases, including cleaning raw data, defining features, dividing time scales, and extracting events.

Section 4: Mining Segment-Wise Periodic Patterns
Description 4: Explain the methods for mining segment-wise periodicity in TSDB, including the integration of data cube and Apriori data-mining techniques.

Section 5: Stage-2: Data-Mining and Post-processing
Description 5: Outline the steps for data mining and post-processing, from constructing information-theoretic models to extracting and validating association rules.

Section 6: Case Study: Stock-Market Data
Description 6: Present a case study on the application of time series analysis to stock market data, detailing the dataset and the results obtained from rule extraction and reduction.

Section 7: Summary
Description 7: Summarize the literature survey, covering the methodologies of knowledge discovery in TSDB and mining segment-wise periodic patterns. Highlight the main objectives and key findings from the survey.