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Agent skill

Algo Forecast Exponential

by asgard-ai-platform in asgard-ai-platform/skills

Apply exponential smoothing methods for time series forecasting with weighted moving averages.

MITAuto-check passedData & Analytics

Install Algo Forecast Exponential

skills CLI
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-exponential -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills algo-forecast-exponential --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-forecast-exponential .claude/skills/algo-forecast-exponential && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
algo-forecast-exponential
GitHub stars
242
Token cost
~1.1k tokens
SKILL.md length
374 words
Files
4 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Apply exponential smoothing methods for time series forecasting with weighted moving averages.

  • Works in 4 steps: Input Validation → Core Algorithm → Verification → …
  • The user needs simple
  • SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo Forecast Exponential is an agent skill from asgard-ai-platform/skills. Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/damped-trend.md` and `references/ets-framework.md`).

It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

  • The user needs simple
  • Robust forecasts
  • Implement Holt-Winters for seasonal data
  • Build lightweight forecasting without complex models — even if they say simple forecast

Example prompts

  • “simple forecast”
  • “moving average prediction”
  • “smoothing method”
  • “/algo-forecast-exponential”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Input Validation
  2. Core Algorithm
  3. Verification
  4. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Algo Forecast Exponential loads about 1.1k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 374 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 374 words, ~1,075 tokens.

Download SKILL.mdSave it as .claude/skills/algo-forecast-exponential/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
algo-forecast-exponential
description
Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.
metadata.category
WP-47 時間序列預測
metadata.tags
forecasting, exponential-smoothing, holt-winters, time-series

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

IRON LAW: Smoothing Parameters Control the Bias-Variance Trade-Off
α (level), β (trend), γ (seasonality) range [0,1].
- α near 1: react quickly to changes, noisy forecasts (high variance)
- α near 0: smooth forecasts, slow to adapt (high bias)
Optimize via minimizing MSE on training data (or use information criteria).
Never hand-pick smoothing parameters without validation.
Phase 1: Input Validation

Identify components: level only (SES), level+trend (Holt), level+trend+seasonality (Holt-Winters). Determine: additive vs multiplicative trend/seasonality. Gate: Component structure identified, seasonal period known.

Phase 2: Core Algorithm

Holt-Winters (additive):

  1. Initialize: level₀ = mean(first season), trend₀ = (mean(season 2) - mean(season 1))/s, seasonal₀ from first season deviations
  2. Update equations at each t:
    • Level: ℓₜ = α(yₜ - sₜ₋ₛ) + (1-α)(ℓₜ₋₁ + bₜ₋₁)
    • Trend: bₜ = β(ℓₜ - ℓₜ₋₁) + (1-β)bₜ₋₁
    • Seasonal: sₜ = γ(yₜ - ℓₜ) + (1-γ)sₜ₋ₛ
  3. Forecast: ŷₜ₊ₕ = ℓₜ + h×bₜ + sₜ₊ₕ₋ₛ
Phase 3: Verification

Check: in-sample RMSE, residual patterns. Compare against naive baselines (last value, seasonal naive). Gate: Beats naive baseline, residuals show no systematic pattern.

Phase 4: Output

Return forecasts with smoothed components.

Output Format

json
{
  "forecasts": [{"period": "2025-04", "forecast": 1150, "level": 1100, "trend": 20, "seasonal": 30}],
  "parameters": {"alpha": 0.3, "beta": 0.1, "gamma": 0.15},
  "metadata": {"method": "holt_winters_additive", "seasonal_period": 12, "rmse": 45}
}

Examples

Sample I/O

Input: 36 months of monthly sales, clear upward trend, December spike Expected: Holt-Winters additive. Forecast continues trend with repeated December seasonality.

Show full SKILL.md (152 more words)Show less
Edge Cases
InputExpectedWhy
No trend, no seasonalitySES (α only)Simplest variant suffices
Seasonal amplitude growsUse multiplicativeAdditive would underestimate peaks
Very short series (<2 seasons)SES or Holt onlyCan't estimate seasonality

Gotchas

  • Additive vs multiplicative: If seasonal swings grow proportionally with level, use multiplicative. Wrong choice produces poor forecasts, especially at extremes.
  • Initialization sensitivity: The first season's values set the baseline. Poor initialization from noisy early data propagates through the entire forecast.
  • Damped trend: For long horizons, linear trend extrapolation is unrealistic. Use damped trend (φ parameter) to flatten the trend over time.
  • Multiple seasonalities: Standard Holt-Winters handles one seasonal period. For daily data with weekly AND yearly patterns, use TBATS or STL+ETS.
  • Outlier sensitivity: A single outlier can shift the level estimate significantly (especially with high α). Pre-detect and handle outliers.

References

  • For ETS framework and model selection, see references/ets-framework.md
  • For damped trend variants, see references/damped-trend.md

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in algo-forecast-exponential of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/damped-trend.md
  • references/ets-framework.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Algo Forecast Exponential next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Algo Forecast Exponential compared with similar skills
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StatsmodelszLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause
Timesfm ForecastingzLanqing/codex-claude-academic-skills4.7k3 repos~7.5kAutomated safety check: NotesApache-2.0
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Pensieve Searcharkohut/pensieve1.4k—~8.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Algo Forecast Exponential

What does Algo Forecast Exponential do?

Apply exponential smoothing methods for time series forecasting with weighted moving averages. Algo Forecast Exponential is an agent skill from asgard-ai-platform/skills. Apply exponential smoothing methods for time series forecasting with weighted moving averages.

When should I use Algo Forecast Exponential?

Algo Forecast Exponential fits situations like: the user needs simple; robust forecasts; implement Holt-Winters for seasonal data; build lightweight forecasting without complex models — even if they say simple forecast.

How do I install Algo Forecast Exponential in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-exponential -a claude-code`. Or copy the skill folder (algo-forecast-exponential in asgard-ai-platform/skills) into .claude/skills/algo-forecast-exponential in your project. Claude Code loads it when a task matches its description.

How do I install Algo Forecast Exponential in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-exponential -a codex`. Or copy the skill folder (algo-forecast-exponential in asgard-ai-platform/skills) into .agents/skills/algo-forecast-exponential in your project. Codex loads it when a task matches its description.

Can I use Algo Forecast Exponential in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-forecast-exponential -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-forecast-exponential, .gemini/skills/algo-forecast-exponential, .github/skills/algo-forecast-exponential and .opencode/skills/algo-forecast-exponential in your project.

What does Algo Forecast Exponential need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo Forecast Exponential is instructions for the agent only.

Does Algo Forecast Exponential access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Algo Forecast Exponential safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Algo Forecast Exponential use?

Algo Forecast Exponential is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Algo Forecast Exponential use?

About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.4k tokens, read only when the agent opens those files.

What are the alternatives to Algo Forecast Exponential?

Skills that share tags, products or a category with Algo Forecast Exponential: TimesFM Forecasting (google-research/timesfm, 34k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.7k stars) and Find Hypertable Candidates (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo Forecast Exponential?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.