Try looking at the data here !
https://datamarket.com/data/list/?q=
Looots of time series. I've found some nice anomaly sets in there. Specfically, time series.
Are you a student ? If so, you can request Yahoo's outlier dataset !
Have you checked out the datasets at quandl? They aren't made for outlier detection but you can definitely find anomalies if you just spend a little time looking.
You might also appreciate this project: Numenta Anomaly Benchmark (NAB)
This is taken directly from the readme in NAB/tree/master/data.
NAB Data Corpus
Data are ordered, timestamped, single-valued metrics. All data files contain anomalies, unless otherwise noted.
Real data
realAWSCloudwatch/
AWS server metrics as collected by the AmazonCloudwatch service. Example metrics include CPU Utilization, Network Bytes In, and Disk Read Bytes.
realAdExchange/
Online advertisement clicking rates, where the metrics are cost-per-click (CPC) and cost per thousand impressions (CPM). One of the files is normal, without anomalies.
realKnownCause/
This is data for which we know the anomaly causes; no hand labeling.
- ambient_temperature_system_failure.csv: The ambient temperature in an office
setting.
- cpu_utilization_asg_misconfiguration.csv: From Amazon Web Services (AWS)
monitoring CPU usage – i.e. average CPU usage across a given cluster. When
usage is high, AWS spins up a new machine, and uses fewer machines when usage
is low.
- ec2_request_latency_system_failure.csv: CPU usage data from a server in
Amazon's East Coast datacenter. The dataset ends with complete system failure
resulting from a documented failure of AWS API servers. There's an interesting
story behind this data in the Numenta
blog
- machine_temperature_system_failure.csv: Temperature sensor data of an
internal component of a large, industrial mahcine. The first anomaly is a
planned shutdown of the machine. The second anomaly is difficult to detect and
directly led to the third anomaly, a catastrophic failure of the machine.
- nyc_taxi.csv: Number of NYC taxi passengers, where the five anomalies occur
during the NYC marathon, Thanksgiving, Christmas, New Years day, and a snow
storm. The raw data is from the NYC Taxi and Limousine Commission.
The data file included here consists of aggregating the total number of
taxi passengers into 30 minute buckets.
- rogue_agent_key_hold.csv: Timing the key holds for several users of a
computer, where the anomalies represent a change in the user.
- rogue_agent_key_updown.csv: Timing the key strokes for several users of a
computer, where the anomalies represent a change in the user.
realRogueAgent/
This data represents computer usage patterns for different users, where an
anomaly may occur with a rogue user of the computer.
realTraffic/
Real time traffic data from the Twin Cities Metro area in Minnesota, collected
by the
Minnesota Department of Transportation.
Included metrics include occupancy, speed, and travel time from specific
sensors.
realTweets/
A collection of Twitter mentions of large publicly-traded companies
such as Google and IBM. The metric value represents the number of mentions
for a given ticker symbol every 5 minutes.
Artificial data