25-09-2013, 04:36 PM
Pattern Finding and Pattern Discovery in Time Series
Finding and Pattern Discovery .ppt (Size: 96 KB / Downloads: 59)
Pattern Finding
Problems: given observed patterns O1, O2, … OK, specify which pattern the new data X possess?
Other names: pattern recognition, pattern classification
Examples
Recognition: matching fingerprints of the claimant with those of authorized personnel.
Patterns are known beforehand and are observed/described by
Explicit samples
Similar samples (usually)
Modeling approaches:
Build a model for each pattern
Find the best fit model for new data
Usually require training using observed samples
Pattern Discovery
Patterns are not known
But data which are believed to possess patterns are given
Examples:
Clustering: grouping similar samples into clusters
Associative rule mining: discover certain features that often appear together in data
Pattern Finding in Time Series
Examples
In control, certain pattern of sensor signals indicate critical point of the production process
In stock, certain pattern (up/down) of price indicate the trend of the market
People often have to look at the graph by their own eyes and act accordingly when spotting known pattern
Pattern Modeling in Time Series
Both pattern finding and pattern discovery need modeling
Desired properties of the model
The model can be built or trained using observed data
The similarity of new data and the model can be easily computed
Mixture of HMMs
Assumption:
There are different processes (pattern) that generate the time series
Each process can be represented by a HMM
Mixture of HMMs allows
Packing all pattern models in one place
Identifying the processes that generate the time series
Training be efficiently implemented
Summary
Automated pattern finding and pattern discovery in time series are needed
HMMs and its variants can model time series patterns
Parameters can be efficiently initialized and estimated using observed data