Time Series Analysis
Analyze historical data trends to forecast future values and identify anomalies in time series datasets.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add time-series-analysis npx -- -y @trustedskills/time-series-analysis
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"time-series-analysis": {
"command": "npx",
"args": [
"-y",
"@trustedskills/time-series-analysis"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill enables AI agents to perform statistical analysis on data points collected over time intervals. It helps identify trends, seasonal patterns, and anomalies within datasets that evolve chronologically.
When to use it
- Analyzing daily server traffic logs to predict capacity needs for upcoming weeks.
- Evaluating monthly sales figures to forecast revenue for the next quarter.
- Detecting irregular spikes in sensor readings from industrial machinery.
- Reviewing historical stock prices to identify long-term market trends.
Key capabilities
- Parsing and organizing sequential data points into structured time-series formats.
- Calculating moving averages to smooth out short-term fluctuations.
- Identifying seasonal patterns and cyclical behaviors within the data.
- Generating statistical summaries such as mean, variance, and standard deviation over time windows.
Example prompts
- "Analyze this CSV of hourly temperature readings and highlight any days where the average exceeded 30°C."
- "Calculate a 7-day moving average for the provided sales dataset to visualize recent growth trends."
- "Identify potential anomalies in this electricity usage log that deviate significantly from the historical mean."
Tips & gotchas
Ensure your input data is strictly ordered chronologically, as misaligned timestamps can invalidate trend analysis. This skill works best with clean, continuous data; missing intervals may require preprocessing before accurate pattern recognition occurs.
Tags
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