Overview Use Cases Customer Churn Prediction Time Series Anomaly Detection Event Anomaly Detection Cloud Spend Alerts Personalized Promotions Predictive Modeling Real-Time Forecasting Financial Metrics Forecasting Feature Group Requirements Training Models Evaluating Predictions Predictions Demand Forecasting Cumulative Forecasting Text extraction and classification NLP Powered Search Sentiment Analysis Finetuned LLM ChatLLM Language Detection Image Classification & Detection Object Detection Clustering Timeseries Clustering Sales and Revenue Forecasting Predictive Lead Scoring Personalized Search Personalized Recommendations Related Items Model Drift and Monitoring Tensorflow with Vector Matching Custom Python Model Data Ingestion Streaming Feature Store Vector Store AI Agents Named Entity Recognition Guidelines Optimization Connectors Authentication Getting Started with the Python SDK API Reference API Classes API Inputs Documentation Chat Bot API Search Video Tutorials ChatLLM

Required Feature Group Types

To train a model under this use case, you will need to create feature groups of the following type(s):

Feature Group Type	API Configuration Name	Required	Description
Metrics Timeseries Data	TIMESERIES	True	This dataset corresponds to the historical time series data for all the metrics you are trying to forecast.
Metric Attributes	ITEM_ATTRIBUTES		This dataset corresponds to all the attributes or meta-data that you have about the metrics you are forecasting.

Note: Once you upload the datasets under each Feature Group Type that comply with their respective required schemas, you will need to create Machine learning (ML) features that would be used to train your ML model(s). We use the term, "Feature Group" for a group of ML features (dataset columns) under a specific Feature Group Type. Our system support extensible schemas that enables you to provide any number of additional columns/features that you think are relevant to that Feature Group Type.

Feature Group: Metrics Timeseries Data

This dataset corresponds to the historical time series data for all the metrics you are trying to forecast.

Feature Mapping	Feature Type	Required	Description
ITEM_ID	categorical	Y	The name of the financial metric that you are trying to forecast (e.g., Revenue from a particular product line).
VALUE	numerical	Y	The target value of the financial metric.
DATE	timestamp	Y	Date (day, year or month) that corresponds to the value of the target metric.
FUTURE		N	Known values ahead of time (eg., State Holidays, National Holidays etc.) that can be easily included in the training dataset.
[ATTRIBUTE RELEVANT TO FORECASTING]		Y	Any relevant attribute pertaining to the financial metric being forecasted. For e.g., store locations, number of stores, number of people in sales, etc.

Feature Group: Metric Attributes

This dataset corresponds to all the attributes or meta-data that you have about the metrics you are forecasting.

Feature Mapping	Feature Type	Required	Description
ITEM_ID	categorical	Y	The name of the metric that you are trying to forecast (e.g Revenue from a particular product line).
[ATTRIBUTE RELEVANT TO FINANCIAL METRIC]		Y	Any relevant attribute about the metric. For financial metrics around a particular product line, it could be attributes of the product like brand, size, etc. We suggest providing at least 5-6 attributes per lead and up to a maximum of 1000 attributes.