Sample demand data

If you don't have your own demand data ready, you can use one of our sample datasets to explore Beam and the Forecasts API. Each dataset contains synthetic daily demand data modeled on realistic industry patterns. Use any dataset with your own location: the industry type matters more than the specific location when getting started.

Available datasets

Industry	File
Retail	sample_demand_retail.csv
Restaurants	sample_demand_restaurants.csv
Accommodation	sample_demand_accommodation.csv
Parking	sample_demand_parking.csv
Other	sample_demand_other.csv

PredictHQ is optimized for the following named industries: accommodation, cpg, tourism, marketing, parking, restaurants, retail, and transportation. If your business doesn't fit one of these, use other - the platform applies to any industry, and Beam calibrates to your own demand data either way. See Industry recommendations for guidance on choosing.

Using the sample data

With Beam

Upload the sample data to an existing Beam Analysis using the Upload demand data endpoint:

import requests

analysis_id = "YOUR_ANALYSIS_ID"
PHQ_API_TOKEN = "YOUR_API_TOKEN"

with open("sample_demand_retail.csv", "rb") as f:
    response = requests.post(
        url=f"https://api.predicthq.com/v1/beam/analyses/{analysis_id}/sink",
        headers={
            "Authorization": f"Bearer {PHQ_API_TOKEN}",
            "Content-Type": "text/csv",
        },
        data=f,
    )

print("Upload successful" if response.status_code == 202 else response.json())

See the Beam guides for a full walkthrough.

With Forecasts API

Upload the sample data to an existing forecast model using the Upload demand data endpoint:

import requests
import pandas as pd
import json

model_id = "YOUR_MODEL_ID"
PHQ_API_TOKEN = "YOUR_API_TOKEN"

df = pd.read_csv("sample_demand_retail.csv")

response = requests.post(
    url=f"https://api.predicthq.com/v1/forecasts/models/{model_id}/demand",
    headers={
        "Authorization": f"Bearer {PHQ_API_TOKEN}",
        "Content-Type": "application/json",
    },
    json={"demand": json.loads(df.to_json(orient="records"))},
)

print("Upload successful" if response.status_code == 201 else response.json())

See the Getting started with Forecasts API guide for a full walkthrough including how to create a model and generate a forecast.

We provide these datasets for testing and exploration only. For production use, replace them with your own demand data.