Does this skill require a Nixtla API key?

Yes, you must have a valid NIXTLA_TIMEGPT_API_KEY set in your environment variables to use this skill for generating forecasts.

What are exogenous variables in the context of this skill?

Exogenous variables are external factors—such as weather, holidays, or promotional events—that influence your primary data but are not part of the core time series itself.

Can I integrate multiple external data sources at once?

Yes, the integrator can handle multiple feature columns within your exogenous CSV file, provided they are correctly aligned with the date ('ds') column.

What happens if my exogenous data doesn't cover the future forecast period?

The skill includes error handling that checks for this; TimeGPT requires exogenous values for the entire forecast horizon to generate an accurate augmented prediction.

Nixtla Exogenous Integrator

Name: Nixtla Exogenous Integrator
Author: intent-solutions-io

byintent-solutions-io

データサイエンスとML

Integrates external variables like holidays and weather data into TimeGPT models to significantly improve time series forecasting accuracy.

概要

The Nixtla Exogenous Integrator is a specialized skill that enables Claude to enhance time series predictions by incorporating external datasets known as exogenous variables. It automates the complex process of aligning historical time series with external factors such as holiday calendars, weather patterns, or marketing events. By validating data consistency and managing the TimeGPT API interaction, this skill allows developers to generate more nuanced forecasts that reflect real-world influences, complete with automated data cleaning and visualization outputs.

主な機能

Seamless integration of holidays, weather, and event-based variables
Pre-computation validation for forecast horizon data completeness
Generation of augmented forecast CSVs and visual comparison plots
0 GitHub stars
Automatic temporal alignment of historical and exogenous dataframes
Automated TimeGPT API orchestration with exogenous feature support

ユースケース

Predicting retail sales fluctuations during major national holidays
Forecasting utility demand based on historical and predicted weather conditions
Adjusting inventory levels based on scheduled marketing events or economic indicators

概要

主な機能

Seamless integration of holidays, weather, and event-based variables
Pre-computation validation for forecast horizon data completeness
Generation of augmented forecast CSVs and visual comparison plots
0 GitHub stars
Automatic temporal alignment of historical and exogenous dataframes
Automated TimeGPT API orchestration with exogenous feature support

ユースケース

Predicting retail sales fluctuations during major national holidays
Forecasting utility demand based on historical and predicted weather conditions
Adjusting inventory levels based on scheduled marketing events or economic indicators