examples.md

Example Settings and Expected Results

Demo training and inference of TimeGen with prototypes and text

python training_inference.py \
  --base text_control.yaml \
  --gpus 0, \
  --logdir ./logs/  \
  -sl 168 \
  -up \
  -nl 16 \
  --batch_size 128 \
  -lr 0.0001  \
  --use_text \

Demo training and inference of TimeGen with prototypes

python training_inference.py \
  --base multi_domain_timedp.yaml  \
  --gpus 0,  \
  --logdir ./logs/ \
  -sl 168  \
  -up  \
  -nl 16  \
  --batch_size 128 \
  -lr 0.0001  \

Demo training and inference of TimeGen with text

python train_inference.py \
  --base text_control.yaml\
  --gpus 0,  \
  --logdir ./logs/ \
  -sl 168  \
  -nl 16  \
  --batch_size 128 \
  -lr 0.0001  \
  -use_text \

Demo training and inference of TimeGen without text and prototype

python train_inference.py \
  --base multi_domain_timedp.yaml\
  --gpus 0,  \
  --logdir ./logs/ \
  -sl 168  \
  -nl 16  \
  --batch_size 128 \
  -lr 0.0001  \

Dataset

The Electricity dataset is a public multivariate time series dataset widely used for forecasting, anomaly detection, and energy consumption analysis. It contains 15-minute interval electricity consumption records (in kWh) from 370 industrial and residential clients of a Portuguese energy provider, collected between 2011 and 2014. The diverse consumption patterns make it ideal for evaluating machine learning models in multivariate time series forecasting and classification.

Example Output

We evaluate TimeGen and baseline models on time series generation tasks. The metrics used are Maximum Mean Discrepancy (MDD) and Kullback-Leibler divergence (K-L), both measuring the similarity between the generated and real data distributions—lower values indicate better performance. TimeGen consistently outperforms existing baselines across both metrics. Combining prototypes and text leads to the best results, showing the advantage of integrating structured temporal patterns with semantic information.

Model	mdd	k-l
TimeGen with prototypes and text	0.222	0.012
TimeGen with prototypes	0.237	0.016
TimeGen with text	0.288	0.021
TimeGAN	1.631	1.389
GT-GAN	1.290	0.956
TimeVAE	0.978	0.206

Note: The BRIDGE implementation of TimeGen uses a much smaller training dataset compared to TimeDP, due to trade-offs in handling large-scale textual data.