The Anomaly Detection API from Nixtla — 1 operation(s) for anomaly detection.
openapi: 3.1.0
info:
title: Nixtla Forecast Anomaly Detection API
description: API for TimeGPT forecast. Just send your data as json and get results. We do the heavy lifting.
version: 0.2.8
servers:
- url: https://api.nixtla.io
tags:
- name: Anomaly Detection
paths:
/v2/anomaly_detection:
post:
summary: Foundational Time Series Model Multi Series Anomaly Detector
description: Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.
operationId: v2_anomaly_detection_v2_anomaly_detection_post
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/AnomalyDetectionInput'
examples:
- freq: D
level: 90
series:
sizes:
- 35
y:
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 0
- 1
- 2
- 10
- 4
- 5
- 6
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/AnomalyDetectionOutput'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- HTTPBearer: []
x-fern-sdk-method-name: v2/anomaly_detection
tags:
- Anomaly Detection
components:
schemas:
AnomalyDetectionOutput:
properties:
input_tokens:
type: integer
minimum: 0
title: Input Tokens
output_tokens:
type: integer
minimum: 0
title: Output Tokens
finetune_tokens:
type: integer
minimum: 0
title: Finetune Tokens
mean:
items:
type: number
type: array
title: Mean
sizes:
items:
type: integer
type: array
title: Sizes
intervals:
anyOf:
- additionalProperties:
items:
type: number
type: array
type: object
- type: 'null'
title: Intervals
weights_x:
anyOf:
- items:
type: number
type: array
- type: 'null'
title: Weights X
feature_contributions:
anyOf:
- items:
items:
type: number
type: array
type: array
- type: 'null'
title: Feature Contributions
anomaly:
items:
type: boolean
type: array
title: Anomaly
type: object
required:
- input_tokens
- output_tokens
- finetune_tokens
- mean
- sizes
- anomaly
title: AnomalyDetectionOutput
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
input:
title: Input
ctx:
type: object
title: Context
type: object
required:
- loc
- msg
- type
title: ValidationError
SeriesWithFutureExogenous:
properties:
X_future:
anyOf:
- items:
items:
anyOf:
- type: number
- type: string
type: array
type: array
- type: 'null'
title: X Future
description: Future values of the exogenous features. Each feature must be a list of size number of series times the forecast horizon (h).
X:
anyOf:
- items:
items:
anyOf:
- type: number
- type: string
type: array
type: array
- type: 'null'
title: X
description: Historic values of the exogenous features. Each feature must be a list of the same size as the target (y).
categorical_exog:
anyOf:
- items:
type: integer
minimum: 0
type: array
- type: 'null'
title: Categorical Exog
description: Zero-based indices of the columns in X that are categorical features.
y:
items:
type: number
type: array
title: Y
description: Historic values of the target.
sizes:
items:
type: integer
type: array
title: Sizes
description: Sizes of the individual series.
type: object
required:
- y
- sizes
title: SeriesWithFutureExogenous
AnomalyDetectionInput:
properties:
series:
$ref: '#/components/schemas/SeriesWithFutureExogenous'
freq:
type: string
title: Freq
description: The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.
model:
type: string
title: Model
description: Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
default: timegpt-1
clean_ex_first:
type: boolean
title: Clean Ex First
description: A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.
default: true
finetuned_model_id:
anyOf:
- type: string
pattern: ^[a-zA-Z0-9\-_]{1,36}$
- type: 'null'
title: Finetuned Model Id
description: ID of previously finetuned model
multivariate:
type: boolean
title: Multivariate
description: Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.
default: false
model_parameters:
anyOf:
- additionalProperties: true
type: object
- type: 'null'
title: Model Parameters
description: 'Optional dictionary of parameters to customize the behavior of the large time model. '
hist_exog:
anyOf:
- items:
type: integer
minimum: 0
type: array
- type: 'null'
title: Hist Exog
description: Zero-based indices of the exogenous features to treat as historical.
level:
anyOf:
- type: integer
exclusiveMaximum: 100
minimum: 0
- type: number
exclusiveMaximum: 100
minimum: 0
title: Level
description: 'Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.'
default: 99
feature_contributions:
type: boolean
title: Feature Contributions
description: Compute the exogenous features contributions to the forecast.
default: false
type: object
required:
- series
- freq
title: AnomalyDetectionInput
securitySchemes:
HTTPBearer:
type: http
description: HTTPBearer
scheme: bearer