# Integrate with a Demand Forecast in PowerBI ## Tools ### PredictHQ * [Features API](/getting-started/guides/features-api-guides.md) * [Beam](/integrations/integration-guides/beam-data-science-guide.md) * [Predicted Impact Area](/api/impact-area/get-impact-area.md) * [WebApp](https://control.predicthq.com) * Tutorial: [Improving Demand Forecasting Models with Event Features](/getting-started/guides/tutorials/improving-demand-forecasting-models-with-event-features.md) ### Microsoft * [PowerBI AutoML](https://learn.microsoft.com/en-us/power-bi/transform-model/dataflows/dataflows-machine-learning-integration) ## Guide {% embed url="" %} Xuxu Wang (CDO) demoing this approach with PowerBI {% endembed %} ### Base Model in PowerBI The starting point is developing a base model in PowerBI (without PredictHQ data). Using a combination of historical data and time trend features, we quickly developed a base model in PowerBI. This initial version yielded a performance of 48%, a good starting point for further enhancement.

Base model performance in PowerBI (without PredictHQ data)

### Improving Base Model Results with PredictHQ From here you should follow the [Improving Demand Forecasting Models with Event Features](/getting-started/guides/tutorials/improving-demand-forecasting-models-with-event-features.md) tutorial book which helps you work out a set of PredictHQ features that are most impactful to your demand using [Beam](/api/beam/overview.md) and [Features API](/api/features/get-features.md). When we have the relevant PredictHQ features we can enhance the model's accuracy. ### Predicted Impact Area Most teams start with a fixed radius when scoping events around a location. The problem is that the distance over which events influence demand varies—by industry, by location type, and by how people actually move in that area. A radius that works in one market will miss impact or introduce noise in another. The Predicted Impact Area API returns a location and industry-specific polygon boundary that reflects where event-driven demand impact actually occurs. Boundaries are calibrated against real demand and event data across industries and geographies. The recommended approach is to use Saved Locations. When you create a location using `origin_geojson` without specifying a `geojson` area, Predicted Impact Area is calculated automatically and stored against that location. You can then use the `location_id` across all PredictHQ APIs—Events, Features, and Beam—without needing to manage the boundary yourself. ```python import httpx response = httpx.get( "https://api.predicthq.com/v1/impact-area/", headers={ "Authorization": "Bearer YOUR_PREDICTHQ_API_TOKEN", "Accept": "application/json", }, params={ "location.origin": "45.5051,-122.6750", "industry": "restaurants", }, ) print(response.json()) ``` ### Demand Decomposition and Anomaly Detection using Beam Next, the Beam API decomposes our demand data into baseline and remainders. This separation allows us to distinguish regular demand from anomalies and understand the factors driving these demand anomalies, providing a foundation for a more targeted forecasting approach.

Beam correlation results shown in the WebApp

### Feature Importance using Beam We then utilized Beam's [Feature Importance API](/api/beam/analyses/get-feature-importance.md) to evaluate the impact of various events on demand fluctuations. This API helped us identify which events significantly influenced demand, informing our model about the types of events to prioritize in our forecasting.

Feature Importance results shown in the WebApp

### Forecast-Ready Features using Features API Finally, using the insights from the Feature Importance API, we employed the Feature API to integrate detailed, relevant event data into our model. This precise merging of event data directly correlated with a notable improvement in our model's performance.

Beam and Features API results

### Improve Model Performance with PredictHQ Data Upon integrating this data into PowerBI, we developed an advanced model that combined both historical data and PredictHQ's event data. The outcome was a significant leap in performance, reaching 75%.

75% performance in PowerBI using PredictHQ data

### Conclusion This demonstration not only underlines the effectiveness of combining PredictHQ's technology with PowerBI but also opens up new avenues for accurate demand forecasting across various industries. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.predicthq.com/integrations/third-party-integrations/integrate-with-a-demand-forecast-in-powerbi.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.