Timeseries Analysis Guide

Learn how to use Time Series Analysis to analyze historical infrastructure data, predict future loads, and detect anomalies.

📈 Go to Time-Series Analysis →

📋 Overview

Time Series Analysis analyzes various metrics (CPU, Memory, Traffic, etc.) collected from servers or databases in chronological order. This allows you to go beyond simply viewing the current status to understanding load patterns at specific event points or preemptively identifying future resource shortages.

📊 Key Analysis Features

1. Dynamic Charts & Comparative Analysis

Custom Timeframes: View data by hour, day, week, or month, and zoom in on specific points for detailed analysis.
Multi-metric Comparison: Overlay CPU usage and network traffic on a single chart to analyze meteorological correlations.

2. AI-driven Load Prediction

Predicts usage trends for the next 7 days based on data from the past 30 days. This helps you plan in advance when server expansion may be necessary.

3. Anomaly Detection

Automatically detects sudden numerical changes that statistically fall outside normal ranges. This is highly effective for identifying signs of service failure or security attacks (such as DDoS).

🛠️ How to Use

Navigate to the GIIP [Analysis & Reports] > [Timeseries Analysis] menu.
Select the Target (Server/Service Group) and Metric you wish to analyze.
Click the [AI Prediction] button above the chart to apply the prediction model.

💡 Tips

Failure Review: Compare log timestamps of failures with timeseries charts to determine if the cause was resource exhaustion or software logic issues.
Cost Optimization: Identify resources with consistently low usage and downsize them according to the
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API Reference

For detailed API specifications of this feature, refer to the separate guide.

📘 Monitoring API Guide

Version: 1.0 Last Updated: 2026-03-19 Source:

giipv3/public/help/timeseries-analysis.en.md