time-series-decomposer

What problem does it solve?

This Skill helps you understand the underlying patterns in your time-series data by breaking it down into its core components: trend, seasonality, and residuals.

Core Features & Use Cases

• Decomposition: Separate time series into trend, seasonal, and residual parts using additive or multiplicative models.
• Analysis: Analyze the strength and characteristics of trend and seasonality.
• Forecasting: Generate basic forecasts based on identified patterns.
• Anomaly Detection: Identify unusual points in the residual component.
• Use Case: Analyze monthly sales data to understand long-term growth (trend), recurring yearly patterns (seasonality), and any unusual spikes or dips (residuals) to improve sales forecasts.

Quick Start

Use the time-series-decomposer skill to decompose the attached file 'sales_data.csv' using the 'revenue' column and a seasonal period of 12.