Pseudo Periodic Synthetic Time Series
Donated on 2/7/1999
This data set is designed for testing indexing schemes in time series databases. The data appears highly periodic, but never exactly repeats itself.
Dataset Characteristics
Univariate, Time-Series
Subject Area
Other
Associated Tasks
-
Feature Type
-
# Instances
100000
# Features
-
Dataset Information
Additional Information
This data set is designed for testing indexing schemes in time series databases. It is a much larger dataset than has been used in any published study (That we are currently aware of). It contains one million data points. The data has been split into 10 sections to facilitate testing (see below). We recommend building the index with 9 of the 100,000-datapoint sections, and randomly extracting a query shape from the 10th section. (Some previously published work seems to have used queries that were also used to build the indexing structure. This will produce optimistic results) The data are interesting because they have structure at different resolutions. Each of the 10 sections where generated by independent invocations of the function: (see equation.gif) Where rand(x) produces a random integer between zero and x. The data appears highly periodic, but never exactly repeats itself. This feature is designed to challenge the indexing structure. The time series are ploted here: (ts1-5.gif), (ts6-10.gif)
Has Missing Values?
No
Variable Information
The data is stored in one ASCII file. There are 10 columns, 100,000 rows. All data points are in the range -0.5 to +0.5. Rows are separated by carriage returns, columns by spaces.
Dataset Files
| File | Size |
|---|---|
| synthetic.data.gz | 4.8 MB |
| ts6-10.gif | 16.2 KB |
| ts1-5.gif | 14.8 KB |
| synthetic.data.html | 5 KB |
| equation.gif | 1.4 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset pseudo_periodic_synthetic_time_series = fetch_ucirepo(id=136) # data (as pandas dataframes) X = pseudo_periodic_synthetic_time_series.data.features y = pseudo_periodic_synthetic_time_series.data.targets # metadata print(pseudo_periodic_synthetic_time_series.metadata) # variable information print(pseudo_periodic_synthetic_time_series.variables)
Keogh, E. & Pazzani, M. (1999). Pseudo Periodic Synthetic Time Series [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5VS43.
Creators
Eamonn Keogh
Michael Pazzani
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.