RollingStandardDeviationAnomalyDetector
Versions
v1.0.0
Basic Information
Class Name: RollingStandardDeviationAnomalyDetector
Title: Rolling Standard Deviation Detector
Version: 1.0.0
Author: Simon Vedder
Organization: OneStream
Creation Date: 2024-02-28
Default Routine Memory Capacity: 2.0 GB
Tags
Anomaly, Time Series, Supervised, ML
Description
Short Description
Constructor for the Rolling Standard Deviation Anomaly Detection Routine.
Long Description
This anomaly detection technique is used to detect volatility shift anomalies. The anomalous dates marked by this routine will indicate to the user that there was a large change in volatility on/after the detected dates. To detect these anomalies, the standard deviation difference is calculated by taking the difference between the standard deviations of a forward and backward rolling window at each point. For any given point, if the standard deviation difference is greater than or less than some value, set by adjusting the threshold constant hyperparameter, the point will be marked as a volatility shift anomaly. Value X is able to be tuned by the user by changing the constant threshold parameter. A higher constant threshold will make this anomaly detector less sensitive.
Use Cases
1. Retail and E-commerce
In the retail and e-commerce sectors, this anomaly detector plays a crucial role in retroactively identifying significant events that impacted business performance. For instance, a sudden spike in volatility might correlate with a viral marketing campaign, a major holiday shopping period, or even external factors like economic shifts or competitor actions. It's possible that these events are undocumented and therefore not yet included in the forecasting process. By identifying them, it allows the user to enrich their existing feature dataset, ideally improving their products' forecastability. Understanding these events through the lens of historical data allows businesses to quantify the impact of specific occurrences on sales and customer behavior. It enables a deeper understanding of how certain events or periods influenced business metrics, allowing companies to better prepare for similar future events, optimize their marketing strategies, and enhance overall decision-making processes. This analytical approach not only aids in maintaining data integrity but also enriches the understanding of market dynamics and consumer responses over time.
2. Manufacturing And Supply Chain
In the manufacturing and supply chain sectors, an anomaly detector that tracks volatility shifts in production metrics can serve as an indicator of a period of poor supply chain performance. If this routine identifies sudden changes in the variability of production rates, quality control metrics, or supply chain performance, businesses can identify issues such as equipment failures, supply shortages, or bottlenecks. Early identification of these issues enables managers to make informed decisions quickly, whether adjusting production schedules, reallocating resources, or initiating maintenance protocols to minimize downtime. This routine also gives managers better insight into their data history and helps them identify events that may affect future forecasting performance. Utilizing this routine effectively on historical data will improve the data's forecastability. This routine has the potential to detect past supply chain failures that resulted in strange behavior from the provided time series. If these failures were identified and resolved, it would lead to a period of time in the series that doesn't accurately reflect the normal behavior of the series, and should therefore be cleaned.
Routine Methods
1. Init (Constructor)
- Method:
__init__-
Type: Constructor
-
Memory Capacity: 2.0 GB
-
Allow In-Memory Execution: No
-
Read Only: No
-
Method Limits: There are no limits to the constructor method. This method simply saves the input parameters to be utilized in subsequent runs of the fit and predict methods.
-
Outputs Dynamic Artifacts: No
-
Short Description:
- Constructor for the Rolling Standard Deviation Anomaly Detection Routine.
-
Detailed Description:
- The rolling standard deviation constructor is used to set the group name, state info, and hyperparameters for this anomaly detection instance.
-
Inputs:
- Required Input
- Hyperparameters: The hyperparameters for double rolling median and double rolling standard deviation anomaly detectors.
- Name:
hyper_parameters - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: DoubleRollingAnomalyDetectionHyperParameters
- Name:
- Group Name: Group name for your anomaly detector, used as an identifier in the output artifact.
- Name:
group_name - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: str
- Name:
- Hyperparameters: The hyperparameters for double rolling median and double rolling standard deviation anomaly detectors.
- Required Input
-
Artifacts: No artifacts are returned by this method
-
2. Fit (Method)
- Method:
fit-
Type: Method
-
Memory Capacity: 2.0 GB
-
Allow In-Memory Execution: No
-
Read Only: No
-
Method Limits: This method performs efficiently across different dataset sizes. Testing at 100 GB of memory shows that with 25,000 targets and 7.4 million rows (monthly data), the method completes in under 10 minutes. With 27,500 targets and 20.1 million rows (daily data), the method completes in under 10 minutes.
-
Outputs Dynamic Artifacts: No
-
Short Description:
- Fit the model.
-
Detailed Description:
- The fit method will take the data input and calculate a backward and forward rolling standard deviation of a specified window size. It then calculates the difference between the two standard deviations for each data point. The interquartile range of this difference is taken and will be used to determine whether data in the predict dataset is a volatility shift anomaly.
-
Inputs:
- Required Input
- Source Data Definition: The source data definition to use.
- Name:
source_data_definition - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: TimeSeriesTableDefinition
- Name:
- Feature Data Definition: The feature data definition to use.
- Name:
feature_data_definitions - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: list[TimeSeriesTableDefinition]
- Name:
- Date Range: The date range to fit anomalies on.
- Name:
time_range - Tooltip:
- Detail:
- If None, entire dataset will be used.
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Detail:
- Type: Optional[StartDateEndDateDefinition]
- Name:
- Source Data Definition: The source data definition to use.
- Required Input
-
Artifacts: No artifacts are returned by this method
-
3. Predict (Method)
- Method:
predict-
Type: Method
-
Memory Capacity: 2.0 GB
-
Allow In-Memory Execution: No
-
Read Only: No
-
Method Limits: This method performs efficiently with various dataset sizes. Testing at 100 GB of memory shows that with 25,000 targets and 7.4 million rows (monthly data), the method completes in under 10 minutes. With 27,500 targets and 20.1 million rows (daily data), the method completes in under 10 minutes.
-
Outputs Dynamic Artifacts: No
-
Short Description:
- Find anomalies using fitted model
-
Detailed Description:
- The predict method will take the data input and calculate a backward and forward rolling standard deviation of a specified window size. It then calculates the difference between the two standard deviations for each data point. The difference of the two rolling window standard deviations will be compared to the IQR calculated in the fit method. If this difference is above or below the IQR by a factor of c (set in the constructor) the point will be marked as an anomaly in the returned anomaly artifacts.
-
Inputs:
- Required Input
- Source Data Definition: The source data definition to use.
- Name:
source_data_definition - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: TimeSeriesTableDefinition
- Name:
- Feature Data Definition: The feature data definition to use.
- Name:
feature_data_definitions - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: list[TimeSeriesTableDefinition]
- Name:
- Date Range: The date range to predict anomalies on.
- Name:
time_range - Tooltip:
- Detail:
- If None, entire dataset will be used.
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Detail:
- Type: Optional[StartDateEndDateDefinition]
- Name:
- State Info Definition: The snapshot name and description. These will be used as identifiers in the snapshot artifact.
- Name:
state_info - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: StateInfoDefinition
- Name:
- Source Data Definition: The source data definition to use.
- Required Input
-
Artifacts:
-
AnomalySnapshot: Parquet file containing data about your anomaly detection run.
- Qualified Key Annotation:
anomaly_snapshot - Aggregate Artifact:
False - In-Memory Json Accessible:
False - File Annotations:
artifacts_/@anomaly_snapshot/data_/data_<int>.parquet- A partitioned set of parquet files where each file will have no more than 1000000 rows.
- Qualified Key Annotation:
-
Specific Anomaly Dates: Parquet file containing data about the specific dates an anomaly was detected.
- Qualified Key Annotation:
anomaly_dates - Aggregate Artifact:
False - In-Memory Json Accessible:
False - File Annotations:
artifacts_/@anomaly_dates/data_/data_<int>.parquet- A partitioned set of parquet files where each file will have no more than 1000000 rows.
- Qualified Key Annotation:
-
Specific Anomaly Instances: Parquet file containing data about the specific anomaly instances that were detected.
- Qualified Key Annotation:
anomaly_instance - Aggregate Artifact:
False - In-Memory Json Accessible:
False - File Annotations:
artifacts_/@anomaly_instance/data_/data_<int>.parquet- A partitioned set of parquet files where each file will have no more than 1000000 rows.
- Qualified Key Annotation:
-
-
Interface Definitions
1. Anomaly Detection Interface
An interface class requiring fit and predict methods to be implemented.
This BaseRoutineInterface class enforces a common interface for all anomaly detection routines. The interface requires each anomaly detection routine to implement a fit method and a predict method with the same input parameters. Each concrete class will have constructor methods where hyperparameters specific to the anomaly detection algorithm may be set, however, this interface does not enforce any specific constructor method.
Interface Methods:
1. Fit
Method Name: fit
Short Description: Abstract Fit Method
Detailed Description: This specifies the necessary input and output parameters for the fit method on all anomaly detection routines. The input parameters contain a source data definition and time range to fit an anomaly detector to.
Inputs:
| Property | Type | Required | Description |
|---|---|---|---|
source_data_definition | #/$defs/TimeSeriesTableDefinition | Yes | The source data definition to use. |
feature_data_definitions | array | Yes | The feature data definition to use. |
time_range | `#/$defs/StartDateEndDateDefinition | null` | No |
Input Schema (JSON):
{
"$defs": {
"CubeViewTabularConnection": {
"description": "Tabular connection backed by an OneStream cube view.\n\nStores only the cube view name. At materialize time the cube view is\nextracted fresh via the DataFlow XBR endpoint and the result is loaded\nthrough DuckDB into a DataFrame.\n\nAnalogous to SqlTabularConnection (pick a table) or FileTabularConnection\n(pick a file): the user selects a pre-configured artifact, not raw config.\n\nThe OneStream application is taken from the SessionInfo (``si.app_name``) -- the single source\nof truth stamped at the request boundary / derived from the job's IAM entrypoint -- so the\nconnection no longer collects an application name from the user.\n\nExample:\n >>> conn = CubeViewTabularConnection(cube_view_name=\"Retail Source Data\")\n >>> df = conn.materialize_tabular_pandas_dataframe()",
"properties": {
"cube_view_name": {
"description": "The name of the OneStream cube view to use as the data source.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.cubeviewtable:CubeViewTabularConnection.get_cube_view_bound_options",
"options_callback_kwargs": null,
"state_name": "cube_view_name",
"title": "Cube View",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"cube_view_name"
],
"title": "CubeViewTabularConnection",
"type": "object"
},
"FileExtensions_": {
"description": "File Extensions.",
"enum": [
".csv",
".tsv",
".psv",
".parquet",
".xlsx"
],
"title": "FileExtensions_",
"type": "string"
},
"FileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_path": {
"description": "The full file path to the file to ingest.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.filetable:FileTabularConnection.get_file_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_path",
"title": "File Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_path"
],
"title": "FileTabularConnection",
"type": "object"
},
"MetaFileSystemConnectionKey": {
"enum": [
"sql-server-routine",
"sql-server-shared"
],
"title": "MetaFileSystemConnectionKey",
"type": "string"
},
"PartitionedFileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_type": {
"$ref": "#/$defs/FileExtensions_",
"description": "The type of files to read from the directory.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "File Type",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"directory_path": {
"description": "The full directory path containing partitioned tabular files.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.partitionedfiletable:PartitionedFileTabularConnection.get_directory_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "Directory Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_type",
"directory_path"
],
"title": "PartitionedFileTabularConnection",
"type": "object"
},
"SqlTabularConnection": {
"properties": {
"database_resource": {
"description": "The name of the database resource to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_resources",
"options_callback_kwargs": null,
"state_name": "database_resource",
"title": "Database Resource",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"database_name": {
"description": "The name of the database to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_schemas",
"options_callback_kwargs": null,
"state_name": "database_name",
"title": "Database Name",
"tooltip": "Detail:\nNote: If you don\u2019t see the database name that you are looking for in this list, it is recommended that you first move the data to be used within a database that is available within this list.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"table_name": {
"description": "The name of the table to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_tables",
"options_callback_kwargs": null,
"state_name": "table_name",
"title": "Table Name",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"database_resource",
"database_name",
"table_name"
],
"title": "SqlTabularConnection",
"type": "object"
},
"StartDateEndDateDefinition": {
"properties": {
"start_date": {
"description": "The inclusive start of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "Start Date",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"end_date": {
"description": "The inclusive end of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "End Date",
"tooltip": "Detail:\nNote, the Seasonal ARIMA Anomaly Detector Routine treats the end date as exclusive.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"start_date",
"end_date"
],
"title": "StartDateEndDateDefinition",
"type": "object"
},
"TabularConnection": {
"description": "A shared parameter base model dedication to tabular connections.",
"properties": {
"tabular_connection": {
"anyOf": [
{
"$ref": "#/$defs/SqlTabularConnection"
},
{
"$ref": "#/$defs/FileTabularConnection"
},
{
"$ref": "#/$defs/PartitionedFileTabularConnection"
},
{
"$ref": "#/$defs/CubeViewTabularConnection"
}
],
"description": "The connection type to use to access the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"tabular_connection"
],
"title": "TabularConnection",
"type": "object"
},
"TimeSeriesTableDefinition": {
"description": "A parameter base model dedicated to loading tabular time series data.",
"properties": {
"data_connection": {
"$ref": "#/$defs/TabularConnection",
"description": "The connection to the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "source_connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"dimension_columns": {
"description": "The columns to use as dimensions.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"type": "string"
},
"list_render_mode": null,
"long_description": null,
"minItems": 1,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Dimension Columns",
"tooltip": "Validation Constraints:\nThe input must have a minimum length of 1.\n\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"date_column": {
"description": "The column to use as the date.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Date Column",
"tooltip": "Detail:\nThe date column must in a DateTime readable format.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"value_column": {
"description": "The column to use as the value.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Value Column",
"tooltip": "Detail:\nThe value column must be a numeric (int, float, double, decimal, etc.) column.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"data_connection",
"dimension_columns",
"date_column",
"value_column"
],
"title": "TimeSeriesTableDefinition",
"type": "object"
}
},
"properties": {
"source_data_definition": {
"$ref": "#/$defs/TimeSeriesTableDefinition",
"description": "The source data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "SourceDefinition",
"title": "Source Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"feature_data_definitions": {
"description": "The feature data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"$ref": "#/$defs/TimeSeriesTableDefinition"
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "FeatureDefinition",
"title": "Feature Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"time_range": {
"anyOf": [
{
"$ref": "#/$defs/StartDateEndDateDefinition"
},
{
"type": "null"
}
],
"default": null,
"description": "The date range to fit anomalies on.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "DateRange",
"title": "Date Range",
"tooltip": "Detail:\nIf None, entire dataset will be used.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"source_data_definition",
"feature_data_definitions"
],
"title": "AnomalyDetectionFitParameters",
"type": "object"
}
Artifacts: No artifacts are returned by this method
2. Predict
Method Name: predict
Short Description: Abstract Predict Method
Detailed Description: This specifies the necessary input and output parameters for the predict method on all anomaly detection routines. The input parameters contain a source data definition and a time range to detect anomalies.
Inputs:
| Property | Type | Required | Description |
|---|---|---|---|
source_data_definition | #/$defs/TimeSeriesTableDefinition | Yes | The source data definition to use. |
feature_data_definitions | array | Yes | The feature data definition to use. |
time_range | `#/$defs/StartDateEndDateDefinition | null` | No |
state_info | #/$defs/StateInfoDefinition | Yes | The snapshot name and description. These will be used as identifiers in the snapshot artifact. |
Input Schema (JSON):
{
"$defs": {
"CubeViewTabularConnection": {
"description": "Tabular connection backed by an OneStream cube view.\n\nStores only the cube view name. At materialize time the cube view is\nextracted fresh via the DataFlow XBR endpoint and the result is loaded\nthrough DuckDB into a DataFrame.\n\nAnalogous to SqlTabularConnection (pick a table) or FileTabularConnection\n(pick a file): the user selects a pre-configured artifact, not raw config.\n\nThe OneStream application is taken from the SessionInfo (``si.app_name``) -- the single source\nof truth stamped at the request boundary / derived from the job's IAM entrypoint -- so the\nconnection no longer collects an application name from the user.\n\nExample:\n >>> conn = CubeViewTabularConnection(cube_view_name=\"Retail Source Data\")\n >>> df = conn.materialize_tabular_pandas_dataframe()",
"properties": {
"cube_view_name": {
"description": "The name of the OneStream cube view to use as the data source.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.cubeviewtable:CubeViewTabularConnection.get_cube_view_bound_options",
"options_callback_kwargs": null,
"state_name": "cube_view_name",
"title": "Cube View",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"cube_view_name"
],
"title": "CubeViewTabularConnection",
"type": "object"
},
"FileExtensions_": {
"description": "File Extensions.",
"enum": [
".csv",
".tsv",
".psv",
".parquet",
".xlsx"
],
"title": "FileExtensions_",
"type": "string"
},
"FileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_path": {
"description": "The full file path to the file to ingest.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.filetable:FileTabularConnection.get_file_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_path",
"title": "File Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_path"
],
"title": "FileTabularConnection",
"type": "object"
},
"MetaFileSystemConnectionKey": {
"enum": [
"sql-server-routine",
"sql-server-shared"
],
"title": "MetaFileSystemConnectionKey",
"type": "string"
},
"PartitionedFileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_type": {
"$ref": "#/$defs/FileExtensions_",
"description": "The type of files to read from the directory.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "File Type",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"directory_path": {
"description": "The full directory path containing partitioned tabular files.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.partitionedfiletable:PartitionedFileTabularConnection.get_directory_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "Directory Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_type",
"directory_path"
],
"title": "PartitionedFileTabularConnection",
"type": "object"
},
"SqlTabularConnection": {
"properties": {
"database_resource": {
"description": "The name of the database resource to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_resources",
"options_callback_kwargs": null,
"state_name": "database_resource",
"title": "Database Resource",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"database_name": {
"description": "The name of the database to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_schemas",
"options_callback_kwargs": null,
"state_name": "database_name",
"title": "Database Name",
"tooltip": "Detail:\nNote: If you don\u2019t see the database name that you are looking for in this list, it is recommended that you first move the data to be used within a database that is available within this list.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"table_name": {
"description": "The name of the table to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_tables",
"options_callback_kwargs": null,
"state_name": "table_name",
"title": "Table Name",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"database_resource",
"database_name",
"table_name"
],
"title": "SqlTabularConnection",
"type": "object"
},
"StartDateEndDateDefinition": {
"properties": {
"start_date": {
"description": "The inclusive start of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "Start Date",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"end_date": {
"description": "The inclusive end of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "End Date",
"tooltip": "Detail:\nNote, the Seasonal ARIMA Anomaly Detector Routine treats the end date as exclusive.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"start_date",
"end_date"
],
"title": "StartDateEndDateDefinition",
"type": "object"
},
"StateInfoDefinition": {
"properties": {
"snapshot_name": {
"description": "The name of the anomaly detector instance.",
"field_type": "input",
"input_component": {
"component_type": "textbox",
"height": null,
"multiline": false
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "snapshot_info",
"title": "Name",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"snapshot_description": {
"description": "The description of your anomaly detector instance.",
"field_type": "input",
"input_component": {
"component_type": "textbox",
"height": null,
"multiline": false
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "snapshot_info",
"title": "Snapshot Description",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"snapshot_name",
"snapshot_description"
],
"title": "StateInfoDefinition",
"type": "object"
},
"TabularConnection": {
"description": "A shared parameter base model dedication to tabular connections.",
"properties": {
"tabular_connection": {
"anyOf": [
{
"$ref": "#/$defs/SqlTabularConnection"
},
{
"$ref": "#/$defs/FileTabularConnection"
},
{
"$ref": "#/$defs/PartitionedFileTabularConnection"
},
{
"$ref": "#/$defs/CubeViewTabularConnection"
}
],
"description": "The connection type to use to access the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"tabular_connection"
],
"title": "TabularConnection",
"type": "object"
},
"TimeSeriesTableDefinition": {
"description": "A parameter base model dedicated to loading tabular time series data.",
"properties": {
"data_connection": {
"$ref": "#/$defs/TabularConnection",
"description": "The connection to the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "source_connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"dimension_columns": {
"description": "The columns to use as dimensions.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"type": "string"
},
"list_render_mode": null,
"long_description": null,
"minItems": 1,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Dimension Columns",
"tooltip": "Validation Constraints:\nThe input must have a minimum length of 1.\n\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"date_column": {
"description": "The column to use as the date.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Date Column",
"tooltip": "Detail:\nThe date column must in a DateTime readable format.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"value_column": {
"description": "The column to use as the value.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Value Column",
"tooltip": "Detail:\nThe value column must be a numeric (int, float, double, decimal, etc.) column.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"data_connection",
"dimension_columns",
"date_column",
"value_column"
],
"title": "TimeSeriesTableDefinition",
"type": "object"
}
},
"description": "\"Note that only most recent fit will be utilized in predictions.\"",
"properties": {
"source_data_definition": {
"$ref": "#/$defs/TimeSeriesTableDefinition",
"description": "The source data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "SourceDefinition",
"title": "Source Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"feature_data_definitions": {
"description": "The feature data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"$ref": "#/$defs/TimeSeriesTableDefinition"
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "FeatureDefinition",
"title": "Feature Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"time_range": {
"anyOf": [
{
"$ref": "#/$defs/StartDateEndDateDefinition"
},
{
"type": "null"
}
],
"default": null,
"description": "The date range to predict anomalies on.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "DateRange",
"title": "Date Range",
"tooltip": "Detail:\nIf None, entire dataset will be used.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"state_info": {
"$ref": "#/$defs/StateInfoDefinition",
"description": "The snapshot name and description. These will be used as identifiers in the snapshot artifact.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "StateInfoDefinition",
"title": "State Info Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"source_data_definition",
"feature_data_definitions",
"state_info"
],
"title": "AnomalyDetectionPredictParameters",
"type": "object"
}
Artifacts:
| Property | Type | Required | Description |
|---|---|---|---|
anomaly_snapshot | unknown | Yes | Parquet file containing data about your anomaly detection run. |
anomaly_dates | unknown | Yes | Parquet file containing data about the specific dates an anomaly was detected. |
anomaly_instance | unknown | Yes | Parquet file containing data about the specific anomaly instances that were detected. |
Artifact Schema (JSON):
{
"additionalProperties": true,
"properties": {
"anomaly_snapshot": {
"description": "Parquet file containing data about your anomaly detection run.",
"io_factory_kwargs": {},
"preview_factory_kwargs": null,
"preview_factory_type": null,
"statistic_factory_kwargs": null,
"statistic_factory_type": null,
"title": "AnomalySnapshot"
},
"anomaly_dates": {
"description": "Parquet file containing data about the specific dates an anomaly was detected.",
"io_factory_kwargs": {},
"preview_factory_kwargs": null,
"preview_factory_type": null,
"statistic_factory_kwargs": null,
"statistic_factory_type": null,
"title": "Specific Anomaly Dates"
},
"anomaly_instance": {
"description": "Parquet file containing data about the specific anomaly instances that were detected.",
"io_factory_kwargs": {},
"preview_factory_kwargs": null,
"preview_factory_type": null,
"statistic_factory_kwargs": null,
"statistic_factory_type": null,
"title": "Specific Anomaly Instances"
}
},
"required": [
"anomaly_snapshot",
"anomaly_dates",
"anomaly_instance"
],
"title": "AnomalyDetectionArtifacts",
"type": "object"
}
Developer Docs
Routine Typename: RollingStandardDeviationAnomalyDetector
| Method Name | Artifact Keys |
|---|---|
__init__ | N/A |
fit | N/A |
predict | anomaly_snapshot, anomaly_dates, anomaly_instance |