Feature-aligned N-BEATS

Official PyTorch Implementation of Feature-aligned N-BEATS with Sinkhorn divergence.

Data

Data should have form of data/$SUPERDOMAIN/$DOMAIN.csv, with three columns:

• time denotes the time index.
• series denotes the series index.
• value denotes the value of the time series at the given time index.

Source

Data used in the paper is obtained from the following sources:

• FRED
  • Commodities category
  • National Income & Product Accounts category
  • Interest Rates category
  • Exchange Rates category
• NCEI (Only 2020s data are used)
  • "TEMP", "STP", "WDSP", "PRCP" columns from Global Surface Summary of the Day - GSOD dataset
  • "TAVG", "AWND", "PRCP" columns from Global Summary of the Month (GSOM), Version 1.0.3 dataset
  • "TAVG", "AWND", "PRCP" columns from Global Summary of the Year (GSOY), Version 1 dataset

Usage

python main.py --source-domains $SOURCE_DOMAIN1 $SOURCE_DOMAIN2 ... \
               --target-domain $TARGET_DOMAIN \
               --forecast-horizon $FORECAST_HORIZON \
               --lookback-multiple $LOOKBACK_MULTIPLE \
               --model $MODEL \
               --loss $LOSS \
               --regularizer $REGULARIZER \
               --temperature $TEMPERATURE \
               --scaler $SCALER \
               --metric $METRIC \
               --learning-rate $LEARNING_RATE \
               --num-lr-cycles $NUM_LR_CYCLES \
               --batch-size $BATCH_SIZE \
               --num-iters $NUM_ITERS \
               --seed $SEED \
               --dtype $DTYPE \
               --data-size $DATA_SIZE

The detailed descriptions about the arguments are as follows:

Argument	Description	Default
source_domains	Source domains ${\mathcal{D}^k}_k$
target_domain	Target domain $\mathcal{D}^T$
forecast_horizon	Forecast horizon $\alpha$	10
lookback_multiple	Lookback multiple $\beta/\alpha$	5
model	Model architecture $\mathfrak{F}$	"NHiTS"
loss	Forecasting loss function $\mathcal{L}$	"SMAPE"
regularizer	Regularizer measure $\mathcal{L}_\mathrm{align}$ NOTE: "None" for vanilla model	"Sinkhorn"
temperature	Regularizing temperature $\lambda$	1.0
scaler	Normalizing function $\sigma$	"softmax"
metric	Evaluation metric for validation and test	"SMAPE"
learning_rate	Learning rate $\eta$	2e-5
num_lr_cycles	Number of learning rate cycles NOTE: torch.optim.lr_scheduler.CyclicLR(mode="triangular2") is used (ref)	50
batch_size	Batch size $B$	2**12
num_iters	Number of iterations	1000
seed	Random seed	0
dtype	Data type used for torch and numpy	"float32"
data_size	Fixed data size for each domain NOTE: "None" to use all data	75000

Citation

@inproceedings{lee2024fanbeats,
  title={Feature-aligned N-BEATS with Sinkhorn divergence},
  author={Lee, Joonhun and Jeon, Myeongho and Kang, Myungjoo and Park, Kyunghyun},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024}
}

Acknowledgement

We would like to acknowledge the significant contributions of the official N-BEATS implementation to our work. Our models are implemented based on their codebase.