timesfm-forecasting

# 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] # 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) uv pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu121 # CPU only uv pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cpu # Apple Silicon (MPS) uv 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" ) ``` ## Output, Configuration, Workflows, and Tuning - [references/output_and_config.md](references/output_and_config.md): reading the point forecast and the 10 quantile bands, deriving prediction intervals, and every `ForecastConfig` field. - [references/workflows.md](references/workflows.md): the standard forecast sequence, many-series forecasting from a wide CSV, and backtesting with interval coverage. - [references/performance_tuning.md](references/performance_tuning.md): GPU and TF32 setup, `per_core_batch_size` by available memory, and memory management. - [references/examples_and_validation.md](references/examples_and_validation.md): runnable examples, the quality checklist, common mistakes, and regression checks. ## 🔗 Integration with Other Skills ### With `statsmodels` Use `statsmodels` for classical models (ARIMA, SARIMAX) as a **comparison baseline**: ```python # TimesFM forecast tfm_point, tfm_q = model.forecast(horizon=H, inputs=[values]) # statsmodels ARIMA forecast from statsmodels.tsa.arima.model import ARIMA arima = ARIMA(values, order=(1,1,1)).fit() arima_forecast = arima.forecast(steps=H) # Compare print(f"TimesFM MAE: {np.mean(np.abs(actual - tfm_point[0])):.2f}") print(f"ARIMA MAE: {np.mean(np.abs(actual - arima_forecast)):.2f}") ``` ### With `matplotlib` / `scientific-visualization` Plot forecasts with prediction intervals as publication-quality figures. ### With `exploratory-data-analysis` Run EDA on the time series before forecasting to understand trends, seasonality, and stationarity. ## 📚 Available Scripts ### `scripts/check_system.py` **Mandatory preflight checker.** Run before first model load. ```bash python scripts/check_system.py ``` Output example: ``` === TimesFM System Requirements Check === [RAM] Total: 32.0 GB | Available: 24.3 GB ✅ PASS [GPU] NVIDIA RTX 4090 | VRAM: 24.0 GB ✅ PASS [Disk] Free: 142.5 GB ✅ PASS [Python] 3.12.1 ✅ PASS [timesfm] Installed (2.5.0) ✅ PASS [torch] Installed (2.4.1+cu121) ✅ PASS VERDICT: ✅ System is ready for TimesFM 2.5 (GPU mode) Recommended: per_core_batch_size=128 ``` ### `scripts/forecast_csv.py` End-to-end CSV forecasting with automatic system check. ```bash python scripts/forecast_csv.py input.csv \ --horizon 24 \ --date-col date \ --value-cols sales,revenue \ --output forecasts.csv ``` ## 📖 Reference Documentation