快速判断

01这个 Skill 是干嘛的: 用 TimesFM 完成时间序列数据准备、预测推断和结果检查。
02它能解决什么问题: 解决预测任务模型接入复杂、数据窗口处理不统一和结果缺少基础验证的问题。
03它适合谁来用: 适合有时间序列数据、需要快速建立预测基线的数据科学家和研究者。

timesfm

用 TimesFM 组织时间序列预测的数据准备、推断与结果检查。

主要能力

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.

使用方式

仓库级安装会保留完整目录；本次核验的入口是 timesfm-forecasting/SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

预测质量高度依赖数据分布，不能跳过基线模型和误差回测。

许可与来源

来源：GitHub 公开仓库。核验许可：Apache-2.0。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

TimesFM

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

Paper:

A decoder-only foundation model for time-series forecasting, ICML 2024.

All checkpoints:

TimesFM Hugging Face Collection.

Google Research blog.
TimesFM in Google 1P Products:

BigQuery ML: Enterprise level SQL queries for scalability and reliability. Google Sheets: For your daily spreadsheet. * Vertex Model Garden: Dockerized endpoint for agentic calling.

This open version is not an officially supported Google product.

Latest Model Version: TimesFM 2.5

Archived Model Versions:

1.0 and 2.0: relevant code archived in the sub directory v1. You can `pip

install timesfm==1.3.0` to install an older version of this package to load them.

Update - July 2, 2026

Updated PyPI to timesfm=2.0.2. See Install.

Update - Apr. 9, 2026

Added fine-tuning example using HuggingFace Transformers + PEFT (LoRA) — see timesfm-forecasting/examples/finetuning/. Also added unit tests (tests/) and incorporated several community fixes.

Shoutout to @kashif and @darkpowerxo.

Update - Mar. 19, 2026

Huge shoutout to @borealBytes for adding the support for AGENTS! TimesFM SKILL.md is out.

Update - Oct. 29, 2025

Added back the covariate support through XReg for TimesFM 2.5.

Update - Sept. 15, 2025

TimesFM 2.5 is out!

Comparing to TimesFM 2.0, this new 2.5 model:

uses 200M parameters, down from 500M.
supports up to 16k context length, up from 2048.
supports continuous quantile forecast up to 1k horizon via an optional 30M

quantile head.

gets rid of the frequency indicator.
has a couple of new forecasting flags.

Since the Sept. 2025 launch, the following improvements have been completed:

✅ Flax version of the model for faster inference.
✅ Covariate support via XReg (see Oct. 2025 update).
✅ Documentation, examples, and agent skill (see timesfm-forecasting/).
✅ Fine-tuning example with LoRA via HuggingFace Transformers + PEFT (see timesfm-forecasting/examples/finetuning/).
✅ Unit tests for core layers, configs, and utilities (see tests/).

Install

From `PyPI`

# Install the package with torch
pip install timesfm[torch]
# Or with Flax
pip install timesfm[flax]
# And when XReg is needed
pip install timesfm[xreg]

Local Install

Clone the repository:

``shell git clone https://github.com/google-research/timesfm.git cd timesfm ``

Create a virtual environment and install dependencies using uv:

```shell # Create a virtual environment uv venv

# Activate the environment source .venv/bin/activate

# Install the package in editable mode with torch uv pip install -e .[torch] # Or with flax uv pip install -e .[flax] # And when XReg is needed uv pip install -e .[xreg] ```

[Optional] Install your preferred torch / jax backend based on your OS and accelerators

(CPU, GPU, TPU or Apple Silicon).:

for Flax.

Code Example

import torch
import numpy as np
import timesfm

torch.set_float32_matmul_precision("high")

model = timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")

model.compile(
    timesfm.ForecastConfig(
        max_context=1024,
        max_horizon=256,
        normalize_inputs=True,
        use_continuous_quantile_head=True,
        force_flip_invariance=True,
        infer_is_positive=True,
        fix_quantile_crossing=True,
    )
)
point_forecast, quantile_forecast = model.forecast(
    horizon=12,
    inputs=[
        np.linspace(0, 1, 100),
        np.sin(np.linspace(0, 20, 67)),
    ],  # Two dummy inputs
)
point_forecast.shape  # (2, 12)
quantile_forecast.shape  # (2, 12, 10): mean, then 10th to 90th quantiles.

请帮我安装「timesfm」。它是一个 Agent Skill 或仓库级 Skill 包。来源：https://github.com/google-research/timesfm 请先阅读仓库的 README、SKILL.md 与安装说明，再按我当前使用的 Agent 的原生规范安装；如果仓库兼容 Agent Skills CLI，优先执行「npx skills add https://github.com/google-research/timesfm」。不要写死为某一个 Agent 的专用目录，不覆盖已有同名文件。完成后告诉我实际安装位置、启用方式和验证结果。