time-series-analysis

Analyze temporal event and interval data with OPAL timechart binning.

Updated Aug 23, 2026

Search Tags:#logs #dashboard #time-series #trend-analysis #OPAL #timechart

Ready to run

npx skills add https://github.com/rustomax/observe-community-mcp --skill time-series-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: time-series-analysis
Source: https://github.com/rustomax/observe-community-mcp/tree/main/skills/time-series-analysis
Command: npx skills add https://github.com/rustomax/observe-community-mcp --skill time-series-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill analyzes temporal event datasets (logs) and intervals by binning data into time-based chunks and producing time-series visualizations with OPAL timechart, enabling trend discovery and temporal insights.

Core Features & Use Cases

Time-based binning with OPAL timechart to generate time-series data.
Supports bin duration options (1h, 5m, 1d) and custom bins via options(bins: N).
Outputs temporal columns: _c_valid_from, _c_valid_to, _c_bucket and returns multiple rows per group for charts.
Use cases include trend analysis, anomaly detection, and cross-namespace comparisons across datasets.

Quick Start

Use timechart to visualize errors per hour across namespaces: filter contains(body, "error") | make_col namespace:string(resource_attributes."k8s.namespace.name") | timechart 1h, count(), group_by(namespace)

Frequently Asked Questions about time-series-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I visualize log trends over time using OPAL timechart?▼

You can visualize log trends by binning temporal event data into time-based chunks with OPAL timechart. It groups log records into intervals like 1h, 5m, or 1d and outputs temporal columns such as _c_valid_from and _c_bucket for charting.

Can I group time-series data by namespace when analyzing log spikes?▼

Yes, you can group time-series data by namespace to analyze log spikes. By using the group_by option in the timechart command, it returns multiple rows per group, enabling cross-namespace comparisons of temporal events.

What bin durations does OPAL timechart support for interval data analysis?▼

OPAL timechart supports bin durations of 1h, 5m, and 1d for interval data analysis. You can also specify a custom number of bins using the options(bins: N) syntax to control the granularity of your time-series visualization.

How do I detect anomalies in observability logs with time-based binning?▼

You can detect anomalies in observability logs by applying time-based binning to count events over intervals. This process produces time-series outputs that highlight spikes and trends, making it easier to identify abnormal temporal patterns in your datasets.

Does time-series analysis require any specific dependencies or components?▼

No, time-series analysis with OPAL timechart requires no external dependencies or components. It operates natively to parse temporal event and interval data, generating dashboard-ready outputs without additional environment setup.