Unplugg API

Machine Learning / API Key Intermediate HTTPS
Varies by plan (check documentation)

Overview

Unplugg is a forecasting API that predicts future values from your historical time series data using machine learning. You send past measurements and get back forecasted values for upcoming periods automatically. It is handy for predicting demand, resource usage, or any metric that follows a pattern over time.

💡

Beginner Tip

The more historical data you provide — ideally several full seasonal cycles — the more accurate your forecasts will be. Start with the test API at unplu.gg/test_api.html to experiment without creating an account.

Available Data

temperature
weather conditions
humidity
wind speed
forecast data

Example Response

JSON Response
{
  "location": "Tokyo",
  "temperature": {
    "current": 22,
    "feels_like": 24,
    "min": 18,
    "max": 26
  },
  "condition": "Partly Cloudy",
  "humidity": 65,
  "wind": {
    "speed": 12,
    "direction": "NE"
  },
  "forecast": [
    {
      "date": "2025-01-16",
      "high": 25,
      "low": 17,
      "condition": "Sunny"
    }
  ]
}

Field Reference

forecast Predicted future values for the number of periods specified by horizon.
lower_bound Lower confidence interval for each forecasted value.
upper_bound Upper confidence interval for each forecasted value.
model Name of the forecasting model selected by the API for this series.

Implementation Example

const url = "https://unplu.gg/";
// Replace headers or query params with the values required by this API.
const response = await fetch(url, {
  headers: {
  "X-API-Key": "YOUR_API_KEY"
  }
});
if (!response.ok) throw new Error(`Request failed: ${response.status}`);
const data = await response.json();
console.log(data);

What Can You Build?

Note: These code examples are AI-generated and unverified. Always refer to the official API documentation for accurate usage.

Common Errors & Troubleshooting

403 Forbidden API key is missing or invalid.
Include your API key in the X-API-Key header with every request.
Too few data points The series array has fewer points than the minimum required.
Provide at least 10-20 historical data points; more data improves forecast accuracy.
horizon out of range The requested forecast horizon is longer than the API supports.
Limit the horizon to a reasonable multiple of your data frequency (e.g., forecast 7 days when you have months of daily data).

Matrix Score Breakdown

🌐 Reachability 30/30
⚡ Speed 5/20
🔒 Security 15/15
🛠 Developer XP 12/20
✓ Reliability 10/15

Partially tested on Apr 5, 2026

Technical Specifications

Auth API Key
HTTPS REQUIRED
CORS UNKNOWN
Category Machine Learning
Difficulty Intermediate
Verified: 2026-04-04

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