time-series-forecaster

What This Skill Does

The time-series-forecaster skill provides a robust suite of tools for temporal data analysis, enabling OpenClaw to move beyond simple observations into predictive modeling. By leveraging statistical powerhouses like ARIMA, the enterprise-ready Prophet library, and deep learning architectures like LSTM, the agent can analyze complex datasets to identify trends, isolate seasonal patterns, and generate high-accuracy future predictions. It integrates directly with the SpecWeave workflow, allowing it to manage versioned forecasting artifacts and automated diagnostic reporting for trend decomposition and residuals.

Installation

To integrate this skill into your environment, run the following command within your terminal or OpenClaw interface:

clawhub install openclaw/skills/skills/anton-abyzov/sw-time-series-forecaster

Use Cases

This skill is designed for data-driven environments where temporal accuracy is paramount. Common use cases include:

• Retail: Predicting inventory demand and sales volume based on historical performance and seasonal spikes.
• Finance: Analyzing stock trends and volatility using automated ARIMA modeling.
• Operations: Resource allocation based on cyclical traffic patterns or user growth projections.
• Marketing: Evaluating how external regressors like marketing spend or seasonal weather changes impact overall business performance.

Example Prompts

1. "Analyze the last two years of sales data. Can you detect any seasonal trends and provide a 6-month forecast using Prophet?"
2. "Perform a trend decomposition on the incoming web traffic logs. I need to see the residual diagnostic plots and a 30-day prediction."
3. "Our inventory is fluctuating wildly. Use an LSTM model to predict demand for the next 14 days, accounting for the holiday events in our dataset."

Tips & Limitations

To get the best results, ensure your data is pre-processed and cleaned; while this skill handles missing data via Prophet, deep learning models like LSTM perform significantly better with consistent, normalized inputs. Always verify that your dataset maintains strict temporal ordering, as random shuffling will invalidate the model's predictive capability. For business-critical forecasting, utilize the built-in confidence intervals to manage risk, and remember that deep learning methods often require larger datasets to outperform simpler statistical baselines.