ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons

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[ Paper | Benchmarking | Documentation ]

A wide range of industrial applications desire precise point and distributional forecasting for diverse prediction horizons. ProbTS serves as a benchmarking tool to aid in understanding how advanced time-series models fulfill these essential forecasting needs. It also sheds light on their advantages and disadvantages in addressing different challenges and unveil the possibilities for future research.

To achieve these objectives, ProbTS provides a unified pipeline that implements cutting-edge models from different research threads, including:

• Long-term point forecasting approaches, such as PatchTST, iTransformer, etc.
• Short-term probabilistic forecasting methods, such as TimeGrad, CSDI, etc.
• Recent time-series foundation models for universal forecasting, such as TimesFM, MOIRAI, etc.

Specifically, ProbTS emphasizes the differences in their primary methodological designs, including:

• Supporting point or distributional forecasts
• Using autoregressive or non-autoregressive decoding schemes for multi-step outputs

Available Models 🧩

ProbTS includes both classical time-series models, specializing in long-term point forecasting or short-term distributional forecasting, and recent time-series foundation models that offer zero-shot and arbitrary-horizon forecasting capabilities for new time series.

Classical Time-series Models

Model	Original Eval. Horizon	Estimation	Decoding Scheme	Class Path
Linear	-	Point	Auto / Non-auto	probts.model.forecaster.point_forecaster.LinearForecaster
GRU	-	Point	Auto / Non-auto	probts.model.forecaster.point_forecaster.GRUForecaster
Transformer	-	Point	Auto / Non-auto	probts.model.forecaster.point_forecaster.TransformerForecaster
Autoformer	Long-trem	Point	Non-auto	probts.model.forecaster.point_forecaster.Autoformer
N-HiTS	Long-trem	Point	Non-auto	probts.model.forecaster.point_forecaster.NHiTS
NLinear	Long-trem	Point	Non-auto	probts.model.forecaster.point_forecaster.NLinear
DLinear	Long-trem	Point	Non-auto	probts.model.forecaster.point_forecaster.DLinear
TimesNet	Short- / Long-term	Point	Non-auto	probts.model.forecaster.point_forecaster.TimesNet
PatchTST	Long-trem	Point	Non-auto	probts.model.forecaster.point_forecaster.PatchTST
iTransformer	Long-trem	Point	Non-auto	probts.model.forecaster.point_forecaster.iTransformer
GRU NVP	Short-term	Probabilistic	Auto	probts.model.forecaster.prob_forecaster.GRU_NVP
GRU MAF	Short-term	Probabilistic	Auto	probts.model.forecaster.prob_forecaster.GRU_MAF
Trans MAF	Short-term	Probabilistic	Auto	probts.model.forecaster.prob_forecaster.Trans_MAF
TimeGrad	Short-term	Probabilistic	Auto	probts.model.forecaster.prob_forecaster.TimeGrad
CSDI	Short-term	Probabilistic	Non-auto	probts.model.forecaster.prob_forecaster.CSDI
TSDiff	Short-term	Probabilistic	Non-auto	probts.model.forecaster.prob_forecaster.TSDiffCond

Fundation Models

Model	Any Horizon	Estimation	Decoding Scheme	Class Path
Lag-Llama	✔	Probabilistic	Auto	probts.model.forecaster.prob_forecaster.LagLlama
ForecastPFN	✔	Point	Non-auto	probts.model.forecaster.point_forecaster.ForecastPFN
TimesFM	✔	Point	Auto	probts.model.forecaster.point_forecaster.TimesFM
TTM	✘	Point	Non-auto	probts.model.forecaster.point_forecaster.TinyTimeMixer
Timer	✔	Point	Auto	probts.model.forecaster.point_forecaster.Timer
MOIRAI	✔	Probabilistic	Non-auto	probts.model.forecaster.prob_forecaster.Moirai
UniTS	✔	Point	Non-auto	probts.model.forecaster.point_forecaster.UniTS
Chronos	✔	Probabilistic	Auto	probts.model.forecaster.prob_forecaster.Chronos

Stay tuned for more models to be added in the future.

Setup 🔧

Environment

ProbTS is developed with Python 3.10 and relies on PyTorch Lightning. To set up the environment:

# Create a new conda environment
conda create -n probts python=3.10
conda activate probts

# Install required packages
pip install .
pip uninstall -y probts # recommended to uninstall the root package (optional)

[Optional] For time-series foundation models, you need to install basic packages and additional dependencies:

# Create a new conda environment
conda create -n probts_fm python=3.10
conda activate probts_fm

# Install required packages
pip install .

# Git submodule
git submodule update --init --recursive

# Install additional packages for foundation models
pip install ".[tsfm]"
pip uninstall -y probts # recommended to uninstall the root package (optional)

# For MOIRAI, we fix the version of the package for better performance
cd submodules/uni2ts
git reset --hard fce6a6f57bc3bc1a57c7feb3abc6c7eb2f264301

Optional for TSFMs reproducibility

# For TimesFM, fix the version for reproducibility (optional)
cd submodules/timesfm
git reset --hard 5c7b905

# For Lag-Llama, fix the version for reproducibility (optional)
cd submodules/lag_llama
git reset --hard 4ad82d9

# For TinyTimeMixer, fix the version for reproducibility (optional)
cd submodules/tsfm
git reset --hard bb125c14a05e4231636d6b64f8951d5fe96da1dc

Datasets

• Short-Term Forecasting: We use datasets from GluonTS. Configure the datasets using --data.data_manager.init_args.dataset {DATASET_NAME}. You can choose from multivariate or univariate datasets as per your requirement.

  # Multivariate Datasets
  ['exchange_rate_nips', 'electricity_nips', 'traffic_nips', 'solar_nips', 'wiki2000_nips']
  
  # Univariate Datasets
  ['tourism_monthly', 'tourism_quarterly', 'tourism_yearly', 'm4_hourly', 'm4_daily', 'm4_weekly', 'm4_monthly', 'm4_quarterly', 'm4_yearly', 'm5']

• Long-Term Forecasting: To set up the long-term forecasting datasets, please follow these steps:

  1. Download long-term forecasting datasets from HERE and place them in ./dataset.
  2. [Opt.] Download CAISO and NordPool datasets from DEPTS and place them in ./dataset.

  Configure the datasets using --data.data_manager.init_args.dataset {DATASET_NAME} with the following list of available datasets:

  # Long-term Forecasting
  ['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf', 'caiso', 'nordpool']

  Note: When utilizing long-term forecasting datasets, you must explicitly specify the context_length and prediction_length parameters. For example, to set a context length of 96 and a prediction length of 192, use the following command-line arguments:

  --data.data_manager.init_args.context_length 96 \
  --data.data_manager.init_args.prediction_length 192 \

Checkpoints for Foundation Models

For full reproducibility, we provide the checkpoints for some foundation models as of the paper completion date. Download the checkpoints from here and place them in the ./checkpoints folder.

You can also download the newest checkpoints from the following repositories:

• For Timer, download the checkpoints from its official repository (Google Drive or Tsinghua Cloud) under the folder /path/to/checkpoints/timer/.
• For ForecastPFN, download the checkpoints from its official repository (Google Drive) under the folder /path/to/checkpoints/forecastpfn/.
• For UniTS, download the checkpoints units_x128_pretrain_checkpoint.pth from its official repository under the folder /path/to/checkpoints/units/.
• For Lag-Llama, download the checkpoints lag-llama.ckpt from its huggingface repository under the folder /path/to/checkpoints/lag_llama/.
• For other models, they can be automatically downloaded from huggingface during the first run.

Model	HuggingFace
MOIRAI	Link
Chronos	Link
TinyTimeMixer	Link
TimesFM	Link

Quick Start 🚀

Specify --config with a specific configuration file to reproduce results of point or probabilistic models on commonly used long- and short-term forecasting datasets. Configuration files are included in the config folder.

To run non-universal models:

bash run.sh

To run foundation models:

bash run_tsfm.sh

Short-term Forecasting

For short-term forecasting scenarios, datasets and corresponding context_length and prediction_length are automatically obtained from GluonTS. Use the following command:

python run.py --config config/path/to/model.yaml \
                --data.data_manager.init_args.path /path/to/datasets/ \
                --trainer.default_root_dir /path/to/log_dir/ \
                --data.data_manager.init_args.dataset {DATASET_NAME}

See full DATASET_NAME list:

from gluonts.dataset.repository import dataset_names
print(dataset_names)

Long-term Forecasting

For long-term forecasting scenarios, context_length and prediction_length must be explicitly assigned:

python run.py --config config/path/to/model.yaml \
                --data.data_manager.init_args.path /path/to/datasets/ \
                --trainer.default_root_dir /path/to/log_dir/ \
                --data.data_manager.init_args.dataset {DATASET_NAME} \
                --data.data_manager.init_args.context_length {CTX_LEN} \
                --data.data_manager.init_args.prediction_length {PRED_LEN} 

DATASET_NAME options:

['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf', 'caiso', 'nordpool']

Benchmarking ⚖️

By utilizing ProbTS, we conduct a systematic comparison between studies that focus on point forecasting and those aimed at distributional estimation, employing various forecasting horizons and evaluation metrics. For more details

• Short-term & Long-term Forecasting Benchmarking
• Evaluating Time Series Foundation Models

Documentation 📖

For detailed information on configuration parameters and model customization, please refer to the documentation.

Key Configuration Parameters

• Adjust model and data parameters in run.sh. Key parameters include:

Config Name	Type	Description
trainer.max_epochs	integer	Maximum number of training epochs.
model.forecaster.class_path	string	Forecaster module path (e.g., probts.model.forecaster.point_forecaster.PatchTST).
model.forecaster.init_args.{ARG}	-	Model-specific hyperparameters.
model.num_samples	integer	Number of samples per distribution during evaluation.
model.learning_rate	float	Learning rate.
data.data_manager.init_args.dataset	string	Dataset for training and evaluation.
data.data_manager.init_args.path	string	Path to the dataset folder.
data.data_manager.init_args.scaler	string	Scaler type: identity, standard (z-score normalization), or temporal (scale based on average temporal absolute value).
data.data_manager.init_args.context_length	integer	Length of observation window (required for long-term forecasting).
data.data_manager.init_args.prediction_length	integer	Forecasting horizon length (required for long-term forecasting).
data.data_manager.init_args.var_specific_norm	boolean	If conduct per-variate normalization or not.
data.batch_size	integer	Batch size.

• To print the full pipeline configuration to a file:

  python run.py --print_config > config/pipeline_config.yaml

Acknowledgement 🌟

Special thanks to the following repositories for their open-sourced code bases and datasets.

Tools/Packages

• GluonTS
• PyTorch-TS
• TSLib
• NeuralForecast

Official Implementations

Classical Time-series Models

• Autoformer
• N-HiTS
• NLinear, DLinear
• TimesNet
• RevIN
• PatchTST
• iTransformer
• GRU NVP, GRU MAF, Trans MAF, TimeGrad
• CSDI
• TSDiff

Time-series Foundation Models

• MOIRAI
• Chronos
• Lag-Llama
• TimesFM
• Timer
• UniTS
• ForecastPFN
• TTM

Citing ProbTS 🌟

If you have used ProbTS for research or production, please cite it as follows.

@article{zhang2023probts,
  title={{ProbTS}: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons},
  author={Zhang, Jiawen and Wen, Xumeng and Zhang, Zhenwei and Zheng, Shun and Li, Jia and Bian, Jiang},
  journal={arXiv preprint arXiv:2310.07446},
  year={2023}
}