Exponential Smoothing

Exponential Smoothing is a Data skill for Claude Code, published by asgard-ai-platform in skills.

228 stars28 forkson asgard-ai-platform/skillsAdded 2026/09/03Repository updated 2026/06/06

ai-agentanthropicclaudeclaude-agent-skillsclaude-codecoding-agentknowledge-basemcpmethodologyopen-sourceprompt-engineeringskillstaiwan

Install in seconds

Install Exponential Smoothing

Copy Exponential Smoothing into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-exponential ~/.claude/skills/algo-forecast-exponential

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/asgard-ai-platform/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: algo-forecast-exponential/SKILL.md in asgard-ai-platform/skills
Installs to: ~/.claude/skills/algo-forecast-exponential
Collection: One of 25 skills cataloged from this repository
Category: Data — 710 skills

What Exponential Smoothing does

Exponential Smoothing applies SES, Holt, or Holt-Winters methods to forecast time series with level, trend, and seasonality. Use it for simple short-horizon forecasts and lightweight smoothing when a full model is unnecessary.

Exponential Smoothing is cataloged under Data on DirSkills. Exponential Smoothing comes from a repository tagged ai-agent, anthropic, claude, claude-agent-skills and claude-code.

Documentation

README

Excerpt

Exponential Smoothing

Overview

Exponential smoothing assigns exponentially decreasing weights to past observations. Three variants: Simple (SES, level only), Holt (level + trend), Holt-Winters (level + trend + seasonality). ETS framework (Error-Trend-Seasonality) provides a unified statistical model. Fast, interpretable, and competitive with complex models for short horizons.

When to Use

Trigger conditions:

Quick forecasting with minimal configuration
Short-horizon forecasts (1-2 seasonal cycles ahead)
Data with clear level, trend, and/or seasonal components

When NOT to use:

For long-range forecasts (uncertainty accumulates too fast)
When external regressors are important (use regression or ML models)

Algorithm

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Exponential Smoothing

What else does asgard-ai-platform publish alongside Exponential Smoothing?
Exponential Smoothing is one of 25 skills that DirSkills catalogs from asgard-ai-platform/skills, the repository it ships in. Its siblings there include ARIMA Time Series Model, Ad Bidding Strategies and Ad Budget Allocation. Each one is a separate skill with its own page in this directory, installs the same way Exponential Smoothing does, and is maintained by asgard-ai-platform in that same repository. The rest of the collection is listed on the asgard-ai-platform/skills page.
How does Exponential Smoothing compare to other Data skills?
Exponential Smoothing ranks #645 by stars among the 710 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Exponential Smoothing against them. Open each page to compare what they document and how they install.