Time Series Anomaly Detector

Advanced time series anomaly detection tool using multiple statistical algorithms including Z-score analysis, Interquartile Range (IQR) method, and moving average deviation. Perfect for financial data analysis, IoT sensor monitoring, business metrics tracking, and quality control. Features real-time visualization, customizable sensitivity thresholds, and comprehensive export options.

Multiple Algorithms Real-time Visualization Sensitivity Control Export Results Sample Datasets

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56,154

Financial: Stock price anomaly detection

Sensor: Temperature sensor outlier analysis

Business: Sales performance deviation tracking

Data Input

Input Method

Text CSV File

Time Series Data

Format: timestamp,value (one per line)

Detection Settings

Algorithm

Z-Score Threshold: 2.5

Higher = less sensitive

Time Series Visualization

Upload data to visualize time series and detected anomalies

Detected Anomalies

No anomalies detected yet. Run analysis to see results.

Analysis Statistics

Total Data Points

Anomalies Detected

Anomaly Rate

Algorithm Used

Export Results

Help & Related Tools

Everything you need to know

FAQ Frequently Asked Questions

What anomaly detection algorithms are available?

The tool offers three powerful algorithms: Z-score analysis for statistical outliers, Interquartile Range (IQR) method for robust detection, and Moving Average deviation for trend-based anomalies. Each algorithm can be fine-tuned with custom sensitivity thresholds.

How do I analyze my time series data for anomalies?

Upload your CSV data or paste values directly. The tool automatically detects timestamps and values, then applies your chosen algorithm. Results are displayed with interactive charts highlighting detected anomalies, complete with confidence scores and statistical summaries.

What data formats does the anomaly detector support?

The tool accepts CSV files, direct text input, and JSON format. It automatically handles various timestamp formats including ISO dates, Unix timestamps, and custom date patterns. Works perfectly on all devices and modern browsers.

How accurate is the anomaly detection for business applications?

The algorithms are industry-standard statistical methods used in production environments. Accuracy depends on data quality and chosen sensitivity settings. For critical business decisions, we recommend validating results with domain expertise and using multiple algorithms for confirmation.

Can I customize the sensitivity of anomaly detection?

Yes! Each algorithm offers adjustable sensitivity parameters. Z-score threshold (typically 2-3), IQR multiplier (1.5-3.0), and moving average window size can all be customized to match your specific use case and data characteristics.