patchfm 1.1.1

a Foundation Model for Univariate Time Series Forecasting

A tutorial on how to build a Foundation Model for Univariate Time Series Forecasting

Huggingface Model Card

A transformer-based forecasting model for univariate time series. The approach mirrors Large Language Model (LLM) practices (next-token → next-patch) while remaining lightweight compared to a classic LLM and practical.

Highlights

• Next-patch prediction objective (autoregressive, causal)
• Patch-based representation of time series (tokens ↔ patches)
• Causal masking self-attention with RoPE (relative positions)
• RevIN (Reversible Instance Normalization) with causal statistics
• SwiGLU feed-forward networks
• Multi-quantile outputs (median + uncertainty bands)
• Efficient rollout with KV caching

Installation

pip install patchfm

Quick Start

import torch
from patchfm.configs import PatchFMConfig
from patchfm.model import Forecaster

# --- Instantiate model ---
config = PatchFMConfig()
model = Forecaster(config)

# --- Inference ---
forecast_horizon = 64
seq = torch.randn(1, 1024)  # (batch, time)
pred_median, pred_quantiles = model(seq, forecast_horizon=forecast_horizon, quantiles=[0.1, 0.5, 0.9])  # (batch, time, quantiles)

We provide an extended quick start example in notebooks/tutorial.ipynb. If you dont have suitable hardware you can run the the extended quick start example example also in Google Colab:

Method (TL;DR)

• Patching: Split a context signal of length $w$ into $P_{num} = w / P_{len}$ patches of length $P_{len}$.
• RevIN: Normalize patches using causal running mean/variance over past patches, and denormalize outputs to the original scale.
• Architecture: Input residual MLP → stacked Transformer blocks (MHA + SwiGLU FFN, pre-norm, residual) → $|\mathcal{Q}|$ output heads mapping back to patch space.
• Positional encoding: Rotary Position Embeddings (RoPE) applied to queries/keys.
• Training: Multi-quantile (pinball) loss across positions, elements, and quantiles $\mathcal{Q}$.
• Inference: Predict next patch; roll out autoregressively with KV caching for long horizons.

Problem Formulation

Given context patches $x_{p_1}, \ldots, x_{p_n}$, predict the next patch $x_{p_{i+1}}$ for each position $i$ using only past patches (causality). The model outputs quantiles ${\hat{x}{p{i+1}}^{(q)}: q \in \mathcal{Q}}$ with median (q=0.5) as the point forecast.

Loss: Multi-Quantile (Pinball)

For residual $u = x - \hat{x}^{(q)}$: $$\rho_q(u) = \begin{cases} q,u, & u \ge 0,\ (q-1),u, & u < 0. \end{cases}$$ Aggregate over positions, patch elements, and quantiles.

Architecture

• Input MLP: $\mathbb{R}^{P_{len}} \to \mathbb{R}^{dim}$ residual 2-layer MLP (ReLU)
• Multi-Head Attention: causal mask, RoPE; queries/keys/values per head
• FFN: SwiGLU (SiLU-gated), pre-norm + residual
• Output heads: |Q| linear maps $\mathbb{R}^{dim} \to \mathbb{R}^{P_{len}}$ (one per quantile)

Model Details

• Patch size: 32
• Max context: 32 patches (1024 steps)
• Forecast horizon: 32 steps per forward pass
• Quantiles $\mathcal{Q}$: {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}
• Layers: 6
• Attention heads: 64 (head dim 32)
• Model dim: 2048
• Parameters: ~300M

Inference

• Single step: predict next patch ($P_{len}$ values)
• Long-horizon: append prediction to context and repeat (optionally drop oldest patch to keep window fixed)
• KV caching: reuse cached keys/values for past patches; compute new Q/K/V only for the appended patch

Acknowledgements

We thank the authors of the following repositories for inspiration and code snippets:

• TiRex

Citation

If you use this work, please cite the paper ...