Time Door
APIML-powered API for time series forecasting, anomaly detection, and trend analysis
Overview
Time Door API delivers machine learning-driven time series analysis via RESTful endpoints including forecast, anomaly detection, trend identification, and seasonality decomposition. It accepts JSON input with timestamp-value pairs (univariate/multivariate) and returns structured JSON outputs with predictions, confidence intervals, anomaly flags, and trend metrics. Ideal for integrating into business applications like sales forecasting, IoT sensor data monitoring, inventory demand planning, or financial market trend analysis. Pre-trained ML models optimize for various time series types, with configurable parameters (prediction horizon, sensitivity thresholds) to tailor results to specific use cases.
Example Integration (JavaScript)
fetch('https://timedoor.io')
.then(res => res.json())
.then(data => console.log(data))
.catch(err => console.error(err)); Key Features
- RESTful Architecture
- JSON Input/Output
- ML-powered Forecasting & Anomaly Detection
- Multivariate/Univariate Support
- Configurable Parameters
Frequently Asked Questions
? Is Time Door free to use?
Yes, Time Door offers a free tier with limited monthly requests. Paid plans unlock higher usage limits and advanced features like longer prediction horizons.
? Does it require an API Key?
Yes, you need to register on the Time Door website to obtain an API key for authenticating all requests.
? What is the response format?
All API responses are in JSON format, providing structured data such as predictions, confidence intervals, anomaly flags, and trend analysis results.
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Tool Info
Pros
- ⊕ Easy integration with existing systems
- ⊕ Pre-trained models for quick insights
- ⊕ Scalable for large time series datasets
Cons
- ⊖ Rate-limited free tier
- ⊖ Limited custom model training options
- ⊖ Requires internet connectivity for access