Run non-streaming time-series forecasts via a local Tollama daemon.
npx skills add https://github.com/tollama/tollama --skill tollama-forecast
Please help me install this Agent Skill. Skill: tollama-forecast Source: https://github.com/tollama/tollama/tree/main/skills/tollama-forecast Command: npx skills add https://github.com/tollama/tollama --skill tollama-forecast
This Skill simplifies time-series forecasting by allowing you to run various AI models locally through a unified interface, eliminating the need for complex per-model setups and dependencies.
Use the tollama-forecast skill to run a forecast using the 'mock' model with the input data from './examples/simple_forecast.json'.
High-intent search queries and answers about installing and using this skill.
Run time series forecasts locally by executing bash scripts against a Tollama daemon, which provides a unified API to call foundation models and baselines without individual cloud setups.
Local time series forecasting with foundation models predicts future data points using AI models running directly on your machine, eliminating cloud dependencies while handling optional metrics and covariates.
Yes, you need Python3 to process JSON data for time series forecasting scripts, alongside bash and either the tollama CLI or curl to communicate with the local daemon.
Yes, non-streaming time series forecasts support optional metrics and covariate handling, allowing you to pass contextual variables to models like chronos2 or timesfm through the unified API.
Inspect AI forecasting models and verify daemon health using built-in bash scripts before execution, ensuring your local environment is ready to process historical time series data accurately.
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