nixtla-exogenous-integrator

intent-solutions-io's avatarfrom intent-solutions-io

Incorporates external variables (holidays, weather, events) into TimeGPT forecasts to improve accuracy.Use when forecasts require external data, holidays impact sales, or weather affects demand.Trigger with "include holidays", "add weather data", "integrate events".

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When & Why to Use This Skill

The Nixtla Exogenous Integrator is a high-performance Claude skill designed to enhance TimeGPT time series forecasting by incorporating external variables. By integrating exogenous factors such as holidays, weather patterns, and specific events, it significantly improves predictive accuracy for complex datasets. This tool automates data alignment, handles missing values, and streamlines the TimeGPT API workflow, providing businesses with robust, visualized forecasts that account for real-world external influences.

Use Cases

  • Retail & E-commerce: Improving sales forecasts by integrating holiday calendars to accurately predict and prepare for seasonal demand spikes.
  • Energy & Utilities: Enhancing power load predictions by factoring in temperature and weather variables that directly drive consumption patterns.
  • Supply Chain & Logistics: Factoring in external events and environmental conditions to better anticipate potential delivery delays and inventory requirements.
  • Marketing Analytics: Correlating business KPIs with external promotional events or market-wide trends to generate more accurate ROI projections.
namenixtla-exogenous-integrator
description|
allowed-tools"Read,Write,Bash,Glob,Grep"
version"1.0.0"

Nixtla Exogenous Integrator

Augments TimeGPT forecasts with exogenous variables for increased accuracy.

Purpose

Enhances time series predictions by integrating external data sources like holidays, weather, and events.

Overview

This skill enriches TimeGPT forecasts by considering external factors influencing time series data. It reads exogenous data from CSV files, aligns it with the historical data, and passes both to the TimeGPT API. This integration improves forecast accuracy, especially when events or external conditions affect the time series. The skill outputs an augmented forecast incorporating the impact of these exogenous variables.

Prerequisites

Tools: Read, Write, Bash, Glob, Grep

Environment: NIXTLA_TIMEGPT_API_KEY

Packages:

pip install nixtla pandas matplotlib

Instructions

Step 1: Prepare data

Read historical time series CSV and exogenous variables CSVs. Ensure they have a common date ('ds') column.

Script: {baseDir}/scripts/load_data.py

Usage:

python {baseDir}/scripts/load_data.py data.csv holidays.csv

The loader expects:

  • Historical data: unique_id, ds, y columns
  • Exogenous data: ds column plus feature columns

Step 2: Align data

Merge historical and exogenous dataframes based on the 'ds' column using the alignment script.

Script: {baseDir}/scripts/align_data.py

The script validates that both datasets share a 'ds' column and checks for column name conflicts. It performs a left join to preserve all historical timestamps.

Step 3: Generate forecast

Run the integration script with your data files and forecast parameters.

Script: {baseDir}/scripts/integrate_exogenous.py

Usage:

python {baseDir}/scripts/integrate_exogenous.py \
  --input data.csv \
  --exogenous holidays.csv \
  --horizon 14 \
  --freq D

Parameters:

  • --input: Historical time series CSV file
  • --exogenous: Exogenous variables CSV file (optional)
  • --horizon: Number of periods to forecast
  • --freq: Time series frequency (D=daily, H=hourly, M=monthly, etc.)

The script automatically:

  1. Loads and validates both datasets
  2. Aligns data on the 'ds' column
  3. Prepares exogenous variables (converts to numeric, fills missing values)
  4. Calls TimeGPT API with exogenous features
  5. Saves forecast and generates visualization

Output

  • forecast_exogenous.csv: Predictions with integrated exogenous variables.
  • forecast_plot.png: Visualization of the forecast with historical data and exogenous variables.

Error Handling

  1. Error: Exogenous data missing 'ds' column Solution: Rename the date column in the exogenous data to 'ds'.

  2. Error: Mismatch in date range between historical and exogenous data Solution: Ensure the exogenous data covers the entire historical and forecast period.

  3. Error: Exogenous variables have NaN values in the forecast horizon Solution: Provide future values for exogenous variables for the entire forecast horizon.

  4. Error: TimeGPT API rejected exogenous variables Solution: Ensure exogenous variables are numeric and compatible with the TimeGPT API schema.

  5. Error: NIXTLA_TIMEGPT_API_KEY environment variable not set Solution: Set your TimeGPT API key as an environment variable before running the script.

Examples

Example 1: Holiday impact on sales

Input (data.csv):

unique_id,ds,y
store_1,2024-01-01,100
store_1,2024-01-02,120
store_1,2024-01-03,115

Input (holidays.csv):

ds,holiday
2024-01-01,1
2024-01-02,0
2024-01-03,0
2024-01-04,0

Command:

python {baseDir}/scripts/integrate_exogenous.py \
  --input data.csv \
  --exogenous holidays.csv \
  --horizon 7 \
  --freq D

Output (forecast_exogenous.csv):

unique_id,ds,TimeGPT,TimeGPT-lo-90,TimeGPT-hi-90
store_1,2024-01-04,125,110,140
store_1,2024-01-05,128,113,143

Example 2: Weather affecting demand

Input (data.csv):

unique_id,ds,y
grid_1,2024-01-01 00:00,5000
grid_1,2024-01-01 01:00,5100

Input (weather.csv):

ds,temperature
2024-01-01 00:00,10
2024-01-01 01:00,8
2024-01-01 02:00,7

Command:

python {baseDir}/scripts/integrate_exogenous.py \
  --input data.csv \
  --exogenous weather.csv \
  --horizon 24 \
  --freq H

Output: Hourly forecast incorporating temperature trends.

Resources