TIMEVP: Time Series Visualization and Processing Toolkit
a.k.a. Time is Very imPortant toolkit
by Tian Linger Xu
STEPS to use this toolkit:
- Download a clone of this toolkit to your own labtop. You can click the green button
Clone or downloadto download a ZIP file. Or, if you use Git Bash, simply type in:
git clone https://github.com/lingerxu/timevp.git
Open Matlab and set your working path to the folder containing the downloaded toolkit.
Create a data folder and put all csv files in the data directory.
The csv files contain time series type data. This toolkit supports two types of time series data structure:
stream: a stream of time series data. The csv file should contain a N by 2 matrix. N is the length of the time series. Two columns are [timestamp category_value].
e.g. 344.7000 32.0000 344.8000 34.0000 344.9000 34.0000 345.0000 34.0000 345.1000 34.0000 345.2000 34.0000 345.3000 34.0000 345.4000 32.0000 345.5000 32.0000 345.6000 32.0000
event: time series events with start time and end time. The csv file should contain a N by 3 matrix. N is the total number of events. Three columns are [onset offset category_value].
e.g. 69.0280 69.9450 1.0000 72.5080 73.8050 4.0000 75.4820 87.1540 1.0000 91.3940 104.1530 4.0000 108.3860 111.1130 4.0000 103.1310 121.1620 1.0000 122.7510 123.5740 1.0000 150.0210 153.8760 4.0000 154.0310 155.9760 1.0000
The data files should be stored in the following structure:
- [subject ID]
- [variable3].csv ...
We provided example data under yulab_data
- [subject ID]
Open timevp_config_dataset_info.m to set the data directory to your own data directory and sampling rate of your time series data. If you data was collected at 10 HZ (10 data points per second), then your sampling rate is 0.1. In our example dataset, the time series data were collected at 30 HZ, so the sampling rate is roughly 0.034.
e.g. sample_rate = 0.034; dir_dataset = 'yulab_data';
- Open each demo file and start analyzing your data! Each demo file is independent from each other.
Please see this file for an in-depth tutorial provided at ICIS 2018.