BERT for Time Series: What It Is, How It Works, and How to Train/Fine‑Tune It

Includes accurate visuals from your PDF and a custom dark‑blue chart. Keywords integrated for SEO.

What is a BERT model for time series?

A BERT model is an encoder‑only transformer that learns bidirectional context. TimesBERT adapts BERT for multivariate time series by treating patches as tokens and using functional tokens [DOM], [VAR], and [MASK] to capture sample‑, variate‑, and patch‑level structure.

How BERT works on time series

Tokenization (patch‑wise): split each variate into fixed‑length patches; embed with a linear layer + absolute positional encoding.
Pretraining objectives: Masked Patch Modeling (MPM) + Functional Token Prediction (FTP).
Training setup: AdamW, cosine schedule (1e‑4 → 2e‑7), ~30k steps, batch ≈320, context length 512 with packing.

Tokenization methods

Patch‑wise (default): sizes 36 (classification), 24 (imputation), 4 (anomaly/short‑term)
Point‑wise: extremely long sequences
Frequency‑based (FreqTST): strong for periodicity
LiPCoT: compact representations; effective for biomedical signals

Train / fine‑tune / evaluate

Preprocess: normalize (Z‑score/min‑max), handle missingness
Pretrain: MPM + FTP with settings above
Fine‑tune:
- Classification: use all tokens; [DOM] gives a global representation
- Imputation: predict masked positions directly
- Anomaly detection: reconstruction error as anomaly score
Evaluate (per task): MAE/MSE/RMSE/MAPE/SMAPE; Accuracy/Precision/Recall/F1/ROC‑AUC; F1 for anomalies

SEO keyword clusters

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Figures

Transformer Architecture — Transformer architecture context (from PDF).

TimesBERT pretraining/fine‑tuning — TimesBERT schematic: MPM + FTP and downstream tasks (from PDF).

Tokenization methods — Time Series Tokenization Methods comparison (from PDF).

Metrics and results — Evaluation metrics and benchmark highlights (from PDF).

TimesBERT patch size chart — Custom chart on a dark‑blue palette for direct inclusion.

FAQ

What is a BERT model?

Encoder‑only transformer for bidirectional understanding; in time series, patches are tokens and functional tokens add structure.

How does a BERT model work for time series?

Patch‑wise tokenization + positional encoding → encoder layers → pretraining with MPM + FTP → task‑specific fine‑tuning.

How to train or fine‑tune a BERT model?

Normalize → patch → pretrain (MPM+FTP) with AdamW/cosine → fine‑tune (classification, imputation, anomalies) → evaluate with task‑specific metrics.