Time Series Chart
Time Series Chart
What Is This Report Type?
A Time Series Chart is a specialized area chart optimized for visualizing data measured at successive, equally spaced time intervals. Unlike a standard area chart, the time series variant includes intelligent time-axis handling—automatic interval detection, zoom controls, and timeline navigation—making it the definitive tool for analyzing temporal patterns, trends, cycles, and anomalies across continuous time data.
Why Is It Used?
Time Series Charts are used when time is the primary independent variable and understanding patterns over continuous time is the analytical goal. Standard charts treat time as just another category axis; the time series chart treats it as a continuous scale, enabling zooming, panning, and intelligent aggregation at different time granularities (minutes → hours → days → months → years).
Key Features and Characteristics
| Feature | Description |
|---|---|
| Continuous Time Axis | X-axis is a true continuous time scale, not a categorical axis—enabling smooth zoom transitions. |
| Auto Granularity | Automatically aggregates data to the appropriate interval (hour/day/month) based on zoom level. |
| Zoom & Pan Controls | Built-in time range selector and pan controls for navigating long historical series. |
| Multi-Series Overlay | Multiple metrics can be plotted simultaneously on the same time axis for correlation analysis. |
| Anomaly Visibility | Filled area beneath the line emphasizes deviations and spikes relative to the baseline. |
When to Use It (Use Cases)
- Server Metrics Monitoring: CPU usage, memory, request latency over days/weeks.
- Sales Trend Analysis: Daily or weekly revenue plotted across a full fiscal year.
- IoT Sensor Data: Temperature, pressure, or humidity readings over continuous time streams.
- Financial Forecasting: Historical performance with trend projections for planning cycles.
Real-Time Business Example
Scenario: A platform engineering team monitors API response latency over a 30-day rolling window to detect performance degradations before they impact users.
Visualization: A Time Series Chart plots average response latency (ms) on the Y-axis against a continuous 30-day time axis. The area fill makes baseline latency (80ms) clearly visible, and a sudden spike to 450ms on day 18 stands out immediately as a red filled peak above the baseline. Zooming into that hour reveals the spike coincides with a deployment event on the same day. The team identifies and rolls back the problematic release.
Common Metrics Displayed
- System Metrics: CPU, memory, latency, error rates over time.
- Revenue / Orders: Daily, weekly, or monthly business performance over time.
- User Activity: Session counts, logins, or page views across a continuous timeline.
- Sensor Readings: Any IoT or instrument measurement recorded at regular intervals.
User Interactions
| Interaction | Behavior |
|---|---|
| Filters | Set a custom date range or use presets (Last 7 days, Last 30 days, Last 12 months) to control the time window. |
| Zoom | Drag on the time axis to zoom into a specific period; double-click to reset. |
| Pan | Click and drag the chart area to pan forward or backward through the timeline. |
| Hover / Tooltip | Hovering shows the exact timestamp and value for all series at that point. |
| Granularity Toggle | Switch between hourly, daily, weekly, or monthly aggregation levels. |
| Export | Export to Excel. |
Creation Steps
- Select Time Series as the report type.
- X-Axis (Time Field): Drag a date/timestamp field (e.g., Recorded At or Event Date).
- Metrics: Drag one or more numeric fields for the Y-axis (e.g., Latency, Revenue).
- Granularity: Set the default time interval (hour, day, week, month).