## 23 Multivariate State Estimation Technique - Sequential Probability Ratio Test

The Multivariate State Estimation Technique - Sequential Probability Ratio Test (MSET-SPRT) algorithm monitors critical processes and detects subtle anomalies.

**Related Topics**

- Anomaly Detection
- DBMS_DATA_MINING - Model Settings
- DBMS_DATA_MINING - Algorithm Settings: Multivariate State Estimation Technique - Sequential Probability Ratio Test
- Automatic Data Preparation
- Model Detail View for Multivariate State Estimation Technique - Sequential Probability Ratio Test
- OML4SQL Examples
- OML4R GitHub Examples

### 23.1 About Multivariate State Estimation Technique - Sequential Probability Ratio Test

Multivariate state Estimation Technique - Sequential Probability Ratio Test (MSET-SPRT) is an algorithm for anomaly detection and statistical testing.

MSET is a nonlinear, nonparametric anomaly detection machine learning technique that calibrates the expected behavior of a system based on historical data from the normal operational sequence of monitored signals. It incorporates the learned behavior of a system into a persistent model that represents the normal estimated behavior. You can deploy the model to evaluate a subsequent stream of live signal vectors using Oracle Machine Learning for SQL scoring functions. To form a hypothesis as to the overall health of the system, these functions calculate the difference between the estimated and the actual signal values (residuals) and use SPRT calculations to determine whether any of the signals have become degraded.

To build a good model, MSET requires sufficient historical data that adequately captures all normal modes of behavior of the system. Incomplete data results in false alerts when the system enters a mode of operation that was poorly represented in the historical data. MSET assumes that the characteristics of the data being monitored do not change over time. Once deployed, MSET is a stationary model and does not evolve as it monitors a data stream.

Both MSET and SPRT operate on continuous time-ordered sensor data. If the raw data stream needs to be pre-processed or sampled, you must do that before you pass the data to the MSET-SPRT model.

The `ALGO_MSET_SPRT`

algorithm is designated as a classification machine learning technique. It generates a model in which each data row is labeled as either normal or anomalous. For anomalous predictions, the prediction details provide a list of the sensors that show the anomaly and a weight.

When creating an MSET-SPRT model with the `DBMS_DATA_MINING.CREATE_MODEL`

function, use the `case_id`

argument to provide a unique row identifier for the time-ordered data that the algorithm requires. The build is then able to sort the training data and create windows for sampling and variance estimation. If you do not provide a `case_id`

, then an exception occurs.

MSET-SPRT supports only numeric data. An exception occurs if other column types are in the build data.

When the number of sensors is very high, MSET-SPRT leverages random projections to improve the scalability and robustness of the algorithm. Random projections is a technique that reduces dimensionality while preserving pairwise distances. By randomly projecting the sensor data, the problem is solved in a distance-preserving, lower-dimension space. The MSET hypothesis testing approach is applied on the projected data where each random projection can be viewed as a Monte Carlo simulation of system health. The overall probability of an anomaly follows a binomial distribution with the number of projections as the number of trials and the number of alerting projections as the number of successes.

Note:

An MSET-SPRT model with random projections does not produce prediction details. When random projections are employed, the nature of the prediction output changes. The prediction captures the global health of the system and it is not possible to attribute the cause to individual attributes. Therefore,`PREDICTION_DETAILS`

returns an empty list.
See Also:

DBMS_DATA_MINING - Algorithm Settings: Multivariate State Estimation Technique - Sequential Probability Ratio Test for a listing and explanation of the available model settings.Note:

The term hyperparameter is also interchangeably used for model setting.### 23.2 Score an MSET-SPRT Model

Scoring data with MSET-SPRT models is similar to scoring with classification algorithms, except that the SPRT methodology relies on ordered data because it tracks gradual shifts over multiple MSET predictions.

This is different than the typical usage of Oracle Database SQL prediction functions, which do not keep state information between rows.

The following functions are supported: `PREDICTION`

, `PREDICTION_COST`

, `PREDICTION_DETAILS`

, `PREDICTION_PROBABILITY`

, and `PREDICTION_SET`

. These functions have syntax new in Oracle Database 21c for scoring MSET-SPRT models. That syntax has an `ORDER`

`BY`

clause to order and window the historical data.

The prediction functions return the following information:

`PREDICTION`

indicates whether the record is flagged as anomalous. It uses the same automatically generated labels as one-class SVM models: 1 for normal and 0 for anomalous.`PREDICTION_COST`

performs an auto-cost analysis or a user-specified cost. A user-specified cost typically assigns a higher cost to false positives than to false negatives.`PREDICTION_DETAILS`

specify the signals that support the prediction along with a weight.`PREDICTION_PROBABILITY`

conveys a measure of certainty based on the consolidation logic.`PREDICTION_SET`

returns the set of predictions (0, 1) and the corresponding prediction probabilities for each observation.

Note:

If the values in one or more of the columns specified in the`ORDER`

`BY`

clause are not unique, or do not represent a true chronology of data sample values, the SPRT predictions are not guaranteed to be meaningful or consistent between query executions.
Unlike other classification models, an MSET-SPRT model has no obvious probability measure associated with the anomalous label for the record as a whole. However, the consolidation logic can produce a measure of uncertainty in place of probability. For example, if an alert is raised for 2 anomalies over a window of 5 observations, a certainty of 0.5 is reported when 2 anomalies are seen within the 5 observation window. The certainty increases if more than 3 anomalies are seen and decreases if no anomalies are seen.

The `PREDICTION_DETAILS`

function accommodates output of varying forms and can convey the required information regarding the individual signals that triggered an alarm. When random projections are engaged, only the overall `PREDICTION`

and `PREDICTION_PROBABILITY`

are computed and `PREDICTION_DETAILS`

are not reported.

You must score the historical data in order to tune the SPRT parameters, such as false alerts and miss rates or consolidation logic, before you deploy the MSET model. The SPRT parameters are embedded in the model object to facilitate deployment. While scoring in the database is needed for parameter tuning and forensic analysis on historical data, monitoring a stream of sensor data is more easily done outside of the database in an IoT service or on the edge device itself.

You can build and score an MSET-SPRT model as a partitioned model if the same columns that you use to build the model are present in the input scoring data set. If those columns are not present, the query results in an error.

**Related Topics**