Applications of Time Series Data Mining

A time series is a sequence of data points, measured typically at successive points in time spaced at uniform time intervals. Examples of Time series includes closing value of a company's stock price on a daily basis and company's revenue at the end of each year, etc. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. In terms of data mining, time series analysis can be used for clustering, classification, query by content, anomaly detection as well as forecasting.

Time series data mining combines data mining with time series analysis to:

• Extract features of time series data (such as seasonal patterns, etc.) for building better predictive models.
• Reduce time series data into fewer dimensions by using data mining methods, such as variable selection and clustering.
• Conduct similarity analysis of time series data (pattern detection) for segmenting data, or validating forecasts of new products.

The Applications of Time series data mining include:

Marketing – This application includes predicting the response of a customer to a specific marketing offer. Customers who have reacted positively to a recent offer are more likely to react positively again. Marketing departments across all industries have been using this insight for more targeted customer relationship management. Advanced predictive models often use proxy variables to capture the temporal aspect of the relationship between historical customer behavior and desired future outcome. For example, analysts often manually summarize transactional data about product usage into a set of time series, such as total monthly air minutes, maximum total air use, and change from previous month for mobile phone use. The time series data is then used as input to the predictive models.

Inventory management – Often, time series information is collected on a very granular level in organizations. For example, retailers measure sales of items in a store on the Stock Keeping Unit(SKU) level and in daily time intervals. For stores with thousands of items, this results in a large amount of time series with many records because historical data is sometimes collected over many years. This large amount of data often makes it difficult to extract information relevant for decision-making. TSDM tools help analysts quickly reduce the dimensionality of the problem under investigation and extract relevant information. SKUs with similar sales trends can be combined into segments without losing critical information. Time series analysis techniques, such as smoothing, can help compress detailed information into a picture that makes general patterns easier to spot.

Fraud detection – The similarity analysis provided with TSDM tools work on the most detailed level in order to spot exceptions to average behavior. Credit card providers can use time series data mining to automate the detection of fraudulent behavior in financial transactions. They do this by comparing many detailed transactional time series against a known pattern of abusive behavior. It is often only in looking across the temporal representation of behavior that undesired behavior becomes apparent. The similarity analysis tool can quickly detect behavior over time with known signatures of fraud and create flags for further investigation if similar patterns are detected.

New product forecasting – A never-ending challenge for consumer goods manufacturers and retailers, new product forecasting situations include: predicting entirely new types of products, new markets for existing products and refinements of existing products. All of them require a forecast of future sales without historic data for the new product. However, by using techniques such as similarity analysis, an analyst can examine the demand patterns of past new products having similar attributes and identify the range of demand curves that can be used to model demand for the new product.

http://en.wikipedia.org/wiki/Time_series

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