An anomaly detection and forecasting API. Get started quickly with state-of-the-art algorithms.
An image is available on Docker Hub. Run:
docker run -ti -p=8000:8000 ankane/trend
Download the latest code
git clone https://github.com/ankane/trend-api.git
cd trend-api
Install Jetpack and run:
Rscript -e 'jetpack::install()'
And start the server
Rscript server.R
Detect anomalies in a time series.
- Works with dates and times
- Accounts for seasonality and trend
- Robust to missing values
The current version uses STL with multiple seasonal components for decomposition.
POST /anomalies HTTP/1.1
Host: trendapi.org
Content-Type: application/json
{
"series": {
"2023-01-01": 150,
"2023-01-02": 125,
"2023-01-03": 133
}
}
Returns JSON structured like this:
{
"anomalies": [
"2023-01-10",
"2023-01-13"
]
}
Get future predictions for a time series.
- Works with dates and times
- Accounts for seasonality and trend
- Robust to missing values
- No need to remove outliers beforehand
The current version uses TBATS for predictions.
POST /forecast HTTP/1.1
Host: trendapi.org
Content-Type: application/json
{
"series": {
"2023-01-01": 150,
"2023-01-02": 125,
"2023-01-03": 133
},
"count": 3
}
Returns JSON structured like this:
{
"forecast": {
"2023-03-01": 137.5,
"2023-03-02": 122.9,
"2023-03-03": 144.1
}
}
If you get a flat or linear forecast, this is expected. It means no seasonality is detected in the series.
Get the correlation between two time series.
The current version uses normalized cross correlation.
POST /correlation HTTP/1.1
Host: trendapi.org
Content-Type: application/json
{
"series": {
"2023-01-01": 150,
"2023-01-02": 125,
"2023-01-03": 133
},
"series2": {
"2023-01-01": 150,
"2023-01-02": 176,
"2023-01-03": 145
}
}
Returns JSON structured like this:
{
"correlation": 0.95
}
The API uses HTTP status codes to indicate errors.
Code | Description |
---|---|
400 | There’s an issue with the request parameters |
500 | There’s an issue with the server |
The body will contain details about the specific error.
{
"error": "Missing parameter: series"
}
A client library is available for Ruby.
Here’s an example with jQuery:
var series = {}, i, date, data;
for (i = 1; i < 30; i++) {
date = new Date(2018, 3, i);
series[date.toISOString()] = date.getDay();
}
data = {series: series};
$.post("https://trendapi.org/forecast", data, function(resp) {
console.log(resp);
}, "json");
A special thanks to Rob J Hyndman for his incredible work on forecasting. Learn more about the topic from his free online book.
View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features