Here is my take on it: I use R and its IDE RStudio.
The hard part, cleaning the data, is luckily done. Sharing the CSV via a dropbox link is not bad. The file is well structured. To improve it you could add a licence and provide a bit more information about the source. For more information see our certificates.
If you want to publish in a more "...
You can find data related to the Canadian lynx and snowshoe hare pelt-trading records of the Hudson Bay Company, starting in 1845. It seems to be a standard dataset, described for instance in Predator-Prey Models.
The base repository is Lynx and Hare Data, and you can find for instance the csv file lynxhare.csv.
The Lotka-Volterra Models for predactor/...
I think having separate datasets for each year would probably be the easiest for users of your site. I work with lots of government data portals, and the norm tends to be to have a new dataset each year. It does add a bit of complexity technically, in that a new dataset has to be created each year, but it is much easier for the average citizen to browse this ...
It's hard to suggest a good tool without knowing how deep into programming you want to go, or if the tool is for exploration or presentation.
But here is a sample of many good tools out there:
Pandas (using Matplotlib)
Plotly blog - Time Series Graphs & Eleven ...
The Global Population Dynamics Database has thousands of time series from biological populations recording changes in population size over time. Maybe that would be useful to you?
Prendergast J , Bazeley-White E , Smith O , Lawton J , Inchausti P ,
Kidd D , and Knight S. 2010. The Global Population Dynamics Database.
The KNB Data Repository. (doi:10....
The US Census Bureau provides a good data source:
U.S. Census Bureau's Monthly & Annual Retail Trade
I work at Quandl and here are some databases you might want to check out:
US Census Bureau (free) - Here are the datasets you'll see if you search for "restaurant sales" within this database:
In the paper you link there is Reference 1 http://home.ifi.uio.no/paalh/dataset/alfheim/
which links to a nice page with full data exports for 3 games, including csv and videos (as you said, only home team).
Check out these databases on Quandl:
Australian Bureau of Statistics:
Organisation for Economic Co-Operation and Development with data on Australia: https://www.quandl.com/data/OECD?keyword=construction%20cost%20australia
National Institute of Statistics and Economic Studies with data on France:
I didn't get it: are you looking for synthtic datasets only, or are you looking for real time series data for validating your own ODE models?
In any case, try perhaps
The Long Term Ecological Research Repository
" The Long Term Ecological Research (LTER) program concentrates on studies of ecological processes that play out at time scales spanning ...
Some better known sample data sets
Electricity usage data that reflect different usage patterns common among PG&E customers
Department of Energy Data by Design Challenge sample data
Green Button sample from Texas - two households
OpenEI Energy Datasets (I can't quickly find any Green Button formats)
SDGE (San Diego Gas & Electric) Electric Interval ...
Any space weather data would likely fit the bill. Both NOAA and NASA have links to various data sources.
Most earth weather data would likely also fit what you're asking for (although I'm not as familiar with it). Some simple time series data would be temperature or USGS river gauges.
There has been a bunch of statistical work in this area (spatio-temporal modeling and extreme value theory); there have been several programs at SAMSI that have generated good peer reviewed papers ( http://www.samsi.info/workshop/birs-extreme-events-climate-and-weather-interdisciplinary-workshop-10w5016-0 )
One statistical approach might go like this:
Kaggle once conducted a competition with 22GB of real transaction data:
http://www.kaggle.com/c/acquire-valued-shoppers-challenge/data (registration required)
Exponential Decay formula would work.
is the remaining amout of material of the decay cycle is complete.
If you start with 100 grams of x this is your
If your decay rate is 2% this is your r.
If you do 1 second increments that would be your t.
The remaining amount becomes your new initial amount and you
Initial Amount Time Decay ...
Consider electricity consumption data, which is an interesting for forecasting due to having various types of seasonality (day of week, month, season), temperature, weather and daylight effects, holidays, etc.
Swissgrid, the Swiss Transmisssion System Operator (TSO), publishes a quarter-hourly timeseries for indidvidual regions as well as the entire country....
I'm not sure what qualifies as "large" for you. If you're looking for a sizable collections of time series data, check out the M-series data which have been used in various forecast competitions. The latest one is M3, which have time series of different periodicity.
If you use R, the M ...
Check out enigma.io's 'Public Data Explorer' and search for business license permits. This will result in a long list of locations that you can the use the service's filtering feature to hone in on a small enough area to provide a dataset that is manageable and not massive in size. After doing that, you'll have the address data of license permit issuances ...
There is a classic "Airpassengers" dataset that comes with R.
The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.
Type in data("AirPassengers")
in R studio to get the dataset
Here is an example notebook with scripts to use the data with R.
An excellent source of Covid related data can be found at covidtracking.com. I wouldn't be surprised if you could find what you are looking for there. You can also download complete datasets from covidtracking.com/data/api.
They have been gathering data since the beginning in the US. Every day, 7 days a week, dozens of volunteers scour dept. of health ...
The Oxford COVID-19 Government Response Tracker appears to have the data you want. The dataset covers the whole world and is day-by-day, so you'll need to do some processing to extract just the US state data and merge or filter it to produce week-by-week or month-by-month data.
I publish the Google Play Store statistics of one of my apps:
See for instance the installs per device type. It is fragmented by day, with metrics such as installs/uninstalls/upgrades.
It is less than 100K rows, though, as data is pre-aggregated.
I'm not sure if it fits your criteria, but consider the A Week in the Life of a Browser data set, which combines browsing history with survey data.
Test duration: 7 days
Test type: Global
Firefox versions covered: Fx 3.5 and Fx 3.6, Fx4 Beta
Data submission: 527,817 test sets submitted in November 2010.
Table Name: 'users'
Main table of ...
Since you are looking for only a pendulum, and not a generic time series with random noise (like @blairchristian posted), then I would consider simulating the data.
Here is an example of a pendulum code, although you don't need the visualization part. It's a pretty common coding assignment (not as common as double pendulum it seems), so you can probably ...
I'm not sure the nature of the anomalous patterns you're seeking, so would fine-grain financial streams work for you? I'm assuming you're looking for free datasets. I've used several of the sources listed here: http://www.quantshare.com/sa-426-6-ways-to-download-free-intraday-and-tick-data-for-the-us-stock-market to develop pattern-break models for both ...
A quick search for commodity prices gives this link: https://www.imf.org/external/np/res/commod/External_Data.xls
Please be aware that commodity markets aren't really as random as they may seem - a few smart & devious traders speculate to fleece the heck out of simpleton investors.
What you are looking for is a time series with descriptive metadata. Metadata, in this sense, would be the non-time series values that are never or seldom changing.
For example, the metadata for a power plant includes:
units of measurement
max production capacity
latitude and longitude
The time series data for the same ...