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State-of-art Foundational Time-series models

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# Samay: Time-series Foundational Models Library

Package for training and evaluating time-series foundational models.

Current repository contains the following models:

1. [LPTM](https://arxiv.org/abs/2311.11413)
2. [MOMENT](https://arxiv.org/abs/2402.03885)
3. [TimesFM](https://arxiv.org/html/2310.10688v2)
4. [Chronos](https://arxiv.org/abs/2403.07815)
5. [MOIRAI](https://arxiv.org/abs/2402.02592)
6. [TinytTimeMixers](https://arxiv.org/abs/2401.03955)

More models will be added soon...

## Installation

You can add the package to your project by running the following command:

```bash
pip install git+https://github.com/AdityaLab/Samay.git
```

### Development workflow

To develop on the project, you can clone the repository and install the package in editable mode:

```bash

## Clone repo
git clone https://github.com/AdityaLab/Samay.git

## Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

## Install dependencies
uv sync --reinstall
```

## Usage Examples

Check out example notebooks at https://github.com/AdityaLab/Samay/tree/main/example to quickly get started.

### LPTM

#### Loading Model

```python
from samay.model import LPTMModel

config = {
    "task_name": "forecasting",
    "forecast_horizon": 192,
    "freeze_encoder": True,  # Freeze the patch embedding layer
    "freeze_embedder": True,  # Freeze the transformer encoder
    "freeze_head": False,  # The linear forecasting head must be trained
}
model = LPTMModel(config)
```

#### Loading Dataset

```python
from samay.dataset import LPTMDataset

train_dataset = LPTMDataset(
    name="ett",
    datetime_col="date",
    path="./data/data/ETTh1.csv",
    mode="train",
    horizon=192,
)

finetuned_model = model.finetune(train_dataset)
```

#### Zero-Forecasting

```python
avg_loss, trues, preds, histories = lptm.evaluate(val_dataset)
```

### TimesFM

#### Loading Model

```python
from samay.model import TimesfmModel
from samay.dataset import TimesfmDataset

repo = "google/timesfm-1.0-200m-pytorch"
config = {
    "context_len": 512,
    "horizon_len": 192,
    "backend": "gpu",
    "per_core_batch_size": 32,
    "input_patch_len": 32,
    "output_patch_len": 128,
    "num_layers": 20,
    "model_dims": 1280,
    "quantiles": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
}

tfm = TimesfmModel(config=config, repo=repo)
```

#### Loading Dataset

```python
train_dataset = TimesfmDataset(name="ett", datetime_col='date', path='data/ETTh1.csv', 
                              mode='train', context_len=config["context_len"], horizon_len=128)
val_dataset = TimesfmDataset(name="ett", datetime_col='date', path='data/ETTh1.csv',
                              mode='test', context_len=config["context_len"], horizon_len=config["horizon_len"])
```

#### Zero-Forecasting

```python
avg_loss, trues, preds, histories = tfm.evaluate(val_dataset)
```

### MOIRAI

#### Loading  Model

```python
from samay.dataset import MoiraiDataset
from samay.model import MoiraiTSModel

repo = "Salesforce/moirai-moe-1.0-R-small"
config = {
        "context_len": 128,
        "horizon_len": 64,
        "num_layers": 100,
        "model_type": "moirai-moe",
        "model_size": "small"
    }

moirai_model = MoiraiTSModel(repo=repo, config=config)
```

#### Loading Dataset

```python

train_dataset = MoiraiDataset(name="ett", mode="train", path="data/ETTh1.csv", datetime_col="date", freq="h",
                            context_len=config['context_len'], horizon_len=config['horizon_len'])

test_dataset = MoiraiDataset(name="ett", mode="test", path="data/ETTh1.csv", datetime_col="date", freq="h",
                            context_len=config['context_len'], horizon_len=config['horizon_len'])
```

#### Zero-Forecasting

```python
eval_results, trues, preds, histories = moirai_model.evaluate(test_dataset, metrics=["MSE", "MASE"])
```

### Support

Tested on Python 3.11-3.13 on Linux (CPU + GPU) and MacOS (CPU). Supports NVIDIA GPUs.
Support for Windows and Apple Silicon GPUs is planned.

## Citation

If you use this code in your research, please cite the following paper:

```bibtex
@inproceedings{
kamarthi2024large,
title={Large Pre-trained time series models for cross-domain Time series analysis tasks},
author={Harshavardhan Kamarthi and B. Aditya Prakash},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=vMMzjCr5Zj}
}
```

## Contact

If you have any feedback or questions, you can contact us via email: <hkamarthi3@gatech.edu>, <badityap@cc.gatech.edu>.

Samay

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