{
"data": {
"collective_anomalies": [
{
"t": "2020-01-01T00:00:15Z",
"mean_change_strength": 2.57,
"variance_change_strength": 0.1752,
"size": 10
}
],
"point_anomalies": []
}
}Example response snippet of the Collective And Point Anomalies endpoint.
Time Door is a scalable, resilient, and fully documented API for integrated time series analysis
Matrix Profile computed on 1000 data points with the Time Door API in 1141.18ms.
Time Door provides an expanding set of time series analysis algorithms that can be used to:
Time series data
if missing values
Missing value imputation
if unfulfilled data assumptions
Data transformations
Statistical computation
f(x)Time series data is often incomplete, non-stationary, and fast. Time Door is designed to handle all these properties without any prior data preparation and in one request:
{
"time_series": [
{
"data": {
"2020-01-01T00:00:00Z": -0.1313,
"2020-01-01T00:00:01Z": -0.2547,
"2020-01-01T00:00:02Z": null,
"2020-01-01T00:00:03Z": -0.2562,
"2020-01-01T00:00:04Z": -2.0167,
"2020-01-01T00:00:05Z": -1.3353,
"2020-01-01T00:00:06Z": 1.073,
"2020-01-01T00:00:07Z": 1.0136,
"2020-01-01T00:00:08Z": 1.1977,
"2020-01-01T00:00:09Z": 0.0984,
"2020-01-01T00:00:10Z": 1.0268
},
"imputation": {
"method": "ewma",
"ma_window_size": 10
},
"transformations": {
"box_cox": {
"apply": true,
"lambda": "auto",
"method": "guerrero",
"upper": 2,
"lower": -1
},
"log": {
"apply": false,
"base": "e",
"factor": null,
"constant": 1
},
"seasonal_diff": {
"apply": true,
"period": "auto",
"n_diffs": "auto",
"test": "ss",
"alpha": 0.05
},
"first_diff": {
"apply": true,
"n_diffs": "auto",
"test": "kpss",
"type": "level",
"alpha": 0.05
}
}
},
{
"data": {
"2020-01-01T00:00:00Z": -1.3487,
"2020-01-01T00:00:01Z": 1.0557,
"2020-01-01T00:00:02Z": 1.1499,
"2020-01-01T00:00:03Z": 1.1876,
"2020-01-01T00:00:04Z": 0.3059,
"2020-01-01T00:00:05Z": 0.9625,
"2020-01-01T00:00:06Z": -1.0361,
"2020-01-01T00:00:07Z": -0.567,
"2020-01-01T00:00:08Z": -1.2387,
"2020-01-01T00:00:09Z": -1.3545,
"2020-01-01T00:00:10Z": -1.825
},
"imputation": {
"method": "locf",
"ma_window_size": 10
},
"transformations": {
"box_cox": {
"apply": false,
"lambda": "auto",
"method": "guerrero",
"upper": 2,
"lower": -1
},
"log": {
"apply": true,
"base": 10,
"factor": 10,
"constant": 1
},
"seasonal_diff": {
"apply": false,
"period": "auto",
"n_diffs": "auto",
"test": "ss",
"alpha": 0.05
},
"first_diff": {
"apply": true,
"n_diffs": 1,
"test": "kpss",
"type": "level",
"alpha": 0.05
}
}
}
]
}Example request with 2 time series and individual data imputation and transformation parameters. The API Docs provide full request examples.
Each time series has its own data imputation and transformation parameter set, making sophisticated multivariate time series analysis possible.
{
"reproduction": {
"arguments": {
"method": "mean_var",
"min_ca_size": 10,
"reproduction": true,
"precision": {
"digits": 4,
"method": "significant"
}
},
"time_series": [
{
"imputed": {
"2020-01-01T00:00:10Z": 4.326,
"2020-01-01T00:00:15Z": 4.555
},
"imputation": {
"method": "linear",
"ma_window_size": null
},
"transformations": {
"box_cox": {
"apply": true,
"lambda": 1.245,
"method": "guerrero",
"upper": 2,
"lower": -1
},
"log": {
"apply": false,
"base": "e",
"factor": null,
"constant": 1
},
"seasonal_diff": {
"apply": false,
"period": null,
"n_diffs": "auto",
"test": "ss",
"alpha": 0.05
},
"first_diff": {
"apply": true,
"n_diffs": 1,
"test": "kpss",
"type": "level",
"alpha": 0.05
}
}
}
]
}
}Example response snippet with enabled reproduction (non-default). The API Docs provide full response examples.
For science applications, the Time Door API can provide all information necessary to reproduce the results. In addition to the results of statistical computations, it can return the following data: