This document explain the usage of the Forecasting API.

This API allows to make future prediction based on past and present trends. It uses supervised machine learning techniques to learn the relationship between variables (input) at hand and the variable (the target) we want to forecast.

For instance, it can be used to prediction the production of a manufacturing line or the prediction of a building energy consumption. This API should be used when the target variable is supposed to have an underlying pattern and when historical measurements are available.

This document provides a general tutorial for users who want to consume the forecasting API.

The Forecasting API includes 4 features:

• Learn a model from past input and target data (createModel).
• Apply the model on new data to forecast the target (applyModel).
• Update an existing model with actual data (updateModel).
• Get information on an existing model (getModelInformation).

Take the tour and experiment on your own data prior to any development through our hands-on application! https://try-analytics-se.azurewebsites.net/forecasting

The createModel endpoint creates (train) a forecasting model. The created forecasting model uses information available from driver measurements and process parameters to model the relationship between the drivers and the target we wish to forecast.

The createModel endpoint needs several inputs:

• Historical training data depicting how a system works is available.
• A target variable, i.e., data of special importance for which we want to build a forecasting model, is known.
• Some pattern in the data is suspected, i.e. some relationship between some of the available data and the target can be observed.
• This pattern can be used to create a forecasting model to generate predictions.

Then generated model, learned from the inputs, is then securely stored in Schneider-Electric platform.

The createModel endpoint return the ID of this newly created model for future use.

The applyModel endpoint can be used to apply (score) an existing forecasting model to new data.

The applyModel endpoint needs two different inputs:

• A test data set with the same variables and format as the training set described in the createModel service - The ID of the model created in the createModel service\nThe applyModel endpoint then generates predicted values based on the previously learned model and given the test data set.

This service updates an existing model created with the createModel web service.

The input should include the same variables as the ones used to create the already available prediction model.

Outputs of this service include the updated model ID (whereas the updated model is created with past and updated data), and the same output information as provided in the createModel service.

This service returns information on an existing prediction model that is securely stored on the Schneider-Electric platform.

Amount of calls to the API in the SANDBOX are limited. To get a full experience and extend the thresholds, please subscribe to the API and use the production environment.

Run in Postman collection

First, you need to download the free Postman application for your operating system. Postman currently supports Mac, Windows, and Linux. Once you have Postman installed, you can import the Forecasting API Postman collection by clicking the orange “Run in Postman” button. At this point, you should see the Forecasting API folder in the left-hand column under the Collections tab.

Step 1: Setup environment

Step 2: Prepare Data

Step 3: Create a new forecasting model

Step 4: Train model

Step 5: Apply an existing forecasting model to new data

Step 6: Get model information on an existing prediction model

This API uses API keys to authenticate and allow access to the API.

Your API keys carry many privileges, so be sure to keep them secure!

The API key should be included in all API requests in the Authorization header that looks like the following: | Authorization: your_subscription_key

For steps on how to generate your_subscription_key please refer to the Features tab.

We follow the error response format proposed in RFC 7807 also known as Problem Details for HTTP APIs. As with our normal API responses, your client must be prepared to gracefully handle additional members of the response.

Contact the SE Exchange support team at exchange.support@se.com and include:

• Endpoint URL
• Request/Response
• Any additional information like Screenshots , Postman collections.

Authentication is the act of proving an assertion, such as the identify of a computer system user.