Delay Embedding

Time series modeling and classification based on delay embedding. This is an improved version of "Derivative Delay Embedding: Online Modeling of Streaming Time Series".

• Pre-requisites
• Running the Test
• Preliminary Results
• Folders and Files
• Citation

Pre-requisites

Matlab (the code has been tested on Matlab 2015a)

Running the test

Run MAIN.m

>> MAIN

Preliminary Results

The running print on MSR Action 3D dataset is shown as follow

Processing the MSR_Action3D dataset 
Trained 50 / 284
Trained 100 / 284
Trained 150 / 284
Trained 200 / 284
Trained 250 / 284
tested 50 / 273
tested 100 / 273
tested 150 / 273
tested 200 / 273
tested 250 / 273
Training time: 2.670sec, 0.009sec per sample
Testing time: 16.898sec, 0.062sec per sample
Accuracy = 93.77%

Compare to the state-of-the-art algorithms

	Moving poselets (ICCV2015)	dRNN (ICCV2015)	HBRNN (CVPR2015)	Actionlets & Poselets (CVPR2016)	Our method (2016)
Accuracy	93.6%	92.03%	94.49%*	93.0%	93.77% / 94.52%*

Note: the * marker denotes the results from subsets, which is usually higher than that from the whole dataset

Folders and Files

• DE implements Delay Embedding
• delayEmbeding.m implements 1-D delay embedding
• delayEmbedingND.m implements multi-dimensional delay embedding
• MGM learns Markov Geographic Model
• createGrid.m creates discretized embedding space.
• add2Trans.m records learned transition
• Trans_Prob.m computes transition probability
• HDist.m calculates distance between a testing sample and learned model (transition probability)
• data
• MSR_Action3D.mat is the MSR Action3D dataset
• UCI_CharacterTrajectories.mat is the Character Trajectories Data Set from UCI
• setting_MSR.m and setting_UCI.m are settings for the two datasets used in MAIN.m
• utilities
• confusionMatrix.m plots the confusion matrix
• defaultColors.mat stores the default color map of Matlab
• lowpassFilter.m performs low-pass filter to filter the raw data

Related Work

Zhifei Zhang, Yang Song, Wei Wang, and Hairong Qi. "Derivative Delay Embedding: Online Modeling of Streaming Time Series". The 25th ACM International Conference on Information and Knowledge Management (CIKM), 2016. [PDF]

@inproceedings{zhang2016derivative,
  title={Derivative Delay Embedding: Online Modeling of Streaming Time Series},
  author={Zhang, Zhifei and Song, Yang and Wang, Wei and Qi, Hairong},
  booktitle={Proceedings of the 25th ACM International on Conference on Information and Knowledge Management},
  pages={969--978},
  year={2016},
  organization={ACM}
}

Presentation on CIKM2016. If it is not availabel, please refer to the PDF version.