timesfm-forecasting — quality + safety report

---
name: timesfm-forecasting
description: Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
allowed-tools: Read Write Edit Bash
license: Apache-2.0 license
metadata:
  version: "1.0"
  skill-author: Clayton Young / Superior Byte Works, LLC (@borealBytes)
  skill-version: "1.0.0"
---

# TimesFM Forecasting

## Overview

TimesFM (Time Series Foundation Model) is a pretrained decoder-only foundation model
developed by Google Research for time-series forecasting. It works **zero-shot** — feed it
any univariate time series and it returns point forecasts with calibrated quantile
prediction intervals, no training required.

This skill wraps TimesFM for safe, agent-friendly local inference. It includes a
**mandatory preflight system checker** that verifies RAM, GPU memory, and disk space
before the model is ever loaded so the agent never crashes a user's machine.

> **Key numbers**: TimesFM 2.5 uses 200M parameters (~800 MB on disk, ~1.5 GB in RAM on
> CPU, ~1 GB VRAM on GPU). The archived v1/v2 500M-parameter model needs ~32 GB RAM.
> Always run the system checker first.

## When to Use This Skill

Use this skill when:

- Forecasting **any univariate time series** (sales, demand, sensor, vitals, price, weather)
- You need **zero-shot forecasting** without training a custom model
- You want **probabilistic forecasts** with calibrated prediction intervals (quantiles)
- You have time series of **any length** (the model handles 1–16,384 context points)
- You need to **batch-forecast** hundreds or thousands of series efficiently
- You want a **foundation model** approach instead of hand-tuning ARIMA/ETS parameters

Do **not** use this skill when:

- You need classical statistical models with coefficient interpretation → use `statsmodels`
- You need time series classification or clustering → use `aeon`
- You need multivariate vector autoregression or Granger causality → use `statsmodels`
- Your data is tabular (not temporal) → use `scikit-learn`

> **Note on Anomaly Detection**: TimesFM does not have built-in anomaly detection, but you can
> use the **quantile forecasts as prediction intervals** — values outside the 90% CI (q10–q90)
> are statistically unusual. See the `examples/anomaly-detection/` directory for a full example.

## ⚠️ Mandatory Preflight: System Requirements Check

**CRITICAL — ALWAYS run the system checker before loading the model for the first time.**

```bash
python scripts/check_system.py
```

This script checks:

1. **Available RAM** — warns if below 4 GB, blocks if below 2 GB
2. **GPU availability** — detects CUDA/MPS devices and VRAM
3. **Disk space** — verifies room for the ~800 MB model download
4. **Python version** — requires 3.10+
5. **Existing installation** — checks if `timesfm` and `torch` are installed

> **Note:** Model weights are **NOT stored in this repository**. TimesFM weights (~800 MB)
> download on-demand from HuggingFace on first use and cache in `~/.cache/huggingface/`.
> The preflight checker ensures sufficient resources before any download begins.

```mermaid
flowchart TD
    accTitle: Preflight System Check
    accDescr: Decision flowchart showing the system requirement checks that must pass before loading TimesFM.

    start["🚀 Run check_system.py"] --> ram{"RAM ≥ 4 GB?"}
    ram -->|"Yes"| gpu{"GPU available?"}
    ram -->|"No (2-4 GB)"| warn_ram["⚠️ Warning: tight RAM<br/>CPU-only, small batches"]
    ram -->|"No (< 2 GB)"| block["🛑 BLOCKED<br/>Insufficient memory"]
    warn_ram --> disk
    gpu -->|"CUDA / MPS"| vram{"VRAM ≥ 2 GB?"}
    gpu -->|"CPU only"| cpu_ok["✅ CPU mode<br/>Slower but works"]
    vram -->|"Yes"| gpu_ok["✅ GPU mode<br/>Fast inference"]
    vram -->|"No"| cpu_ok
    gpu_ok --> disk{"Disk ≥ 2 GB free?"}
    cpu_ok --> disk
    disk -->|"Yes"| ready["✅ READY<br/>Safe to load model"]
    disk -->|"No"| block_disk["🛑 BLOCKED<br/>Need space for weights"]

    classDef ok fill:#dcfce7,stroke:#16a34a,stroke-width:2px,color:#14532d
    classDef warn fill:#fef9c3,stroke:#ca8a04,stroke-width:2px,color:#713f12
    classDef block fill:#fee2e2,stroke:#dc2626,stroke-width:2px,color:#7f1d1d
    classDef neutral fill:#f3f4f6,stroke:#6b7280,stroke-width:2px,color:#1f2937

    class ready,gpu_ok,cpu_ok ok
    class warn_ram warn
    class block,block_disk block
    class start,ram,gpu,vram,disk neutral
```

### Hardware Requirements by Model Version

| Model | Parameters | RAM (CPU) | VRAM (GPU) | Disk | Context |
| ----- | ---------- | --------- | ---------- | ---- | ------- |
| **TimesFM 2.5** (recommended) | 200M | ≥ 4 GB | ≥ 2 GB | ~800 MB | up to 16,384 |
| TimesFM 2.0 (archived) | 500M | ≥ 16 GB | ≥ 8 GB | ~2 GB | up to 2,048 |
| TimesFM 1.0 (archived) | 200M | ≥ 8 GB | ≥ 4 GB | ~800 MB | up to 2,048 |

> **Recommendation**: Always use TimesFM 2.5 unless you have a specific reason to use an
> older checkpoint. It is smaller, faster, and supports 8× longer context.

## 🔧 Installation

### Step 1: Verify System (always first)

```bash
python scripts/check_system.py
```

### Step 2: Install TimesFM

```bash
# Using uv (recommended by this repo)
uv pip install timesfm[torch]

# Or using pip
pip install timesfm[torch]

# For JAX/Flax backend (faster on TPU/GPU)
uv pip install timesfm[flax]
```

### Step 3: Install PyTorch for Your Hardware

```bash
# CUDA 12.1 (NVIDIA GPU)
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu121

# CPU only
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cpu

# Apple Silicon (MPS)
pip install torch>=2.0.0  # MPS support is built-in
```

### Step 4: Verify Installation

```python
import timesfm
import numpy as np
print(f"TimesFM version: {timesfm.__version__}")
print("Installation OK")
```

## 🎯 Quick Start

### Minimal Example (5 Lines)

```python
import torch, numpy as np, 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, quantiles = model.forecast(horizon=24, inputs=[
    np.sin(np.linspace(0, 20, 200)),  # any 1-D array
])
# point.shape == (1, 24)        — median forecast
# quantiles.shape == (1, 24, 10) — 10th–90th percentile bands
```

### Forecast from CSV

```python
import pandas as pd, numpy as np

df = pd.read_csv("monthly_sales.csv", parse_dates=["date"], index_col="date")

# Convert each column to a list of arrays
inputs = [df[col].dropna().values.astype(np.float32) for col in df.columns]

point, quantiles = model.forecast(horizon=12, inputs=inputs)

# Build a results DataFrame
for i, col in enumerate(df.columns):
    last_date = df[col].dropna().index[-1]
    future_dates = pd.date_range(last_date, periods=13, freq="MS")[1:]
    forecast_df = pd.DataFrame({
        "date": future_dates,
        "forecast": point[i],
        "lower_80": quantiles[i, :, 2],  # 20th percentile
        "upper_80": quantiles[i, :, 8],  # 80th percentile
    })
    print(f"\n--- {col} ---")
    print(forecast_df.to_string(index=False))
```

### Forecast with Covariates (XReg)

TimesFM 2.5+ supports exogenous variables through `forecast_with_covariates()`. Requires `timesfm[xreg]`.

```python
# Requires: uv pip install timesfm[xreg]
point, quantiles = model.forecast_with_covariates(
    inputs=inputs,
    dynamic_numerical_covariates={"price": price_arrays},
    dynamic_categorical_covariates={"holiday": holiday_arrays},
    static_categorical_covariates={"region": region_labels},
    xreg_mode="xreg + timesfm",  # or "timesfm + xreg"
)
```

| Covariate Type | Description | Example |
| -------------- | ----------- | ------- |
| `dynamic_numerical` | Time-varying numeric | price, temperature, promotion spend |
| `dynamic_categorical` | Time-varying categorical | holiday flag, day of week |
| `static_numerical` | Per-series numeric | store size, account age |
| `static_categorical` | Per-series categorical | store type, region, product category |

**XReg Modes:**
- `"xreg + timesfm"` (default): TimesFM forecasts first, then XReg adjusts residuals
- `"timesfm + xreg"`: XReg fits first, then TimesFM forecasts residuals

> See `examples/covariates-forecasting/` for a complete example with synthetic retail data.

### Anomaly Detection (via Quantile Intervals)

TimesFM does not have built-in anomaly detection, but the **quantile forecasts naturally provide
prediction intervals** that can detect anomalies:

```python
point, q = model.forecast(horizon=H, inputs=[values])

# 90% prediction interval
lower_90 = q[0, :, 1]  # 10th percentile
upper_90 = q[0, :, 9]  # 90th percentile

# Detect anomalies: values outside the 90% CI
actual = test_values  # your holdout data
anomalies = (actual < lower_90) | (actual > upper_90)

# Severity levels
is_warning = (actual < q[0, :, 2]) | (actual > q[0, :, 8])  # outside 80% CI
is_critical = anomalies  # outside 90% CI
```

| Severity | Condition | Interpretation |
| -------- | --------- | -------------- |
| **Normal** | Inside 80% CI | Expected behavior |
| **Warning** | Outside 80% CI | Unusual but possible |
| **Critical** | Outside 90% CI | Statistically rare (< 10% probability) |

> See `examples/anomaly-detection/` for a complete example with visualization.

```python
# Requires: uv pip install timesfm[xreg]
point, quantiles = model.forecast_with_covariates(
    inputs=inputs,
    dynamic_numerical_covariates={"temperature": temp_arrays},
    dynamic_categorical_covariates={"day_of_week": dow_arrays},
    static_categorical_covariates={"region": region_labels},
    xreg_mode="xreg + timesfm",  # or "timesfm + xreg"
)
```

## 📊 Understanding the Output

### Quantile Forecast Structure

TimesFM returns `(point_forecast, quantile_forecast)`:

- **`point_forecast`**: shape `(batch, horizon)` — the median (0.5 quantile)
- **`quantile_forecast`**: shape `(batch, horizon, 10)` — ten slices:

| Index | Quantile | Use |
| ----- | -------- | --- |
| 0 | Mean | Average prediction |
| 1 | 0.1 | Lower bound of 80% PI |
| 2 | 0.2 | Lower bound of 60% PI |
| 3 | 0.3 | — |
| 4 | 0.4 | — |
| **5** | **0.5** | **Median (= `point_forecast`)** |
| 6 | 0.6 | — |
| 7 | 0.7 | — |
| 8 | 0.8 | Upper bound of 60% PI |
| 9 | 0.9 | Upper bound of 80% PI |

### Extracting Prediction Intervals

```python
point, q = model.forecast(horizon=H, inputs=data)

# 80% prediction interval (most common)
lower_80 = q[:, :, 1]  # 10th percentile
upper_80 = q[:, :, 9]  # 90th percentile

# 60% prediction interval (tighter)
lower_60 = q[:, :, 2]  # 20th percentile
upper_60 = q[:, :, 8]  # 80th percentile

# Median (same as point forecast)
median = q[:, :, 5]
```

```mermaid
flowchart LR
    accTitle: Quantile Forecast Anatomy
    accDescr: Diagram showing how the 10-element quantile vector maps to prediction intervals.

    input["📈 Input Series<br/>1-D array"] --> model["🤖 TimesFM<br/>compile + forecast"]
    model --> point["📍 Point Forecast<br/>(batch, horizon)"]
    model --> quant["📊 Quantile Forecast<br/>(batch, horizon, 10)"]
    quant --> pi80["80% PI<br/>q[:,:,1] – q[:,:,9]"]
    quant --> pi60["60% PI<br/>q[:,:,2] – q[:,:,8]"]
    quant --> median["Median<br/>q[:,:,5]"]

    classDef data fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#1e3a5f
    classDef model fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#581c87
    classDef output fill:#dcfce7,stroke:#16a34a,stroke-width:2px,color:#14532d

    class input data
    class model model
    class point,quant,pi80,pi60,median output
```

## 🔧 ForecastConfig Reference

All forecasting behavior is c

… (truncated)