semvec/pypi-packages huggingface dataset:
tw-stock 1.0.7
Get Taiwan's stock info in real-time
License: MIT
Keywords: stock,tw,taiwan stock

Get Taiwan's stock info in real-time

## Install:

```
pip install tw-stock
```
## Example:

```
from tw_stock.stock import get_realtime
print(get_realtime(2330))
```
## Return:

```
{
    "now_amount": "9355",
    "now_diff": "0.0",
    "now_price": "233.00",
    "now_percent": "0.0",
    "stock_name": "台積電",
    "stock_no": "2330"
}
```
mssibyl 0.1.1.dev0
Time Series Forecasting Tool for Machine Learning Researchers with Nerual Process
License: MIT
Keywords: time series forecasting,deep learning,pytorch

# Sibyl: A Time-Seires Forecasting Package 

Sibyl is a package for forecasting time series data with PyTorchLightning which is a lightweight PyTorch wrapper for Machine Learning Researchers.

Sibyl is released and maintained by Microsoft Substrate Connectivity Team.

## Models

- [x] Seq to Seq
- [x] Long-short term memory
- [ ] Neural process
- [ ] Attentive neural process

## Installation

Sibyl is avaialbe on PyPi, feel free to get it via `pip`:

```
pip install mssibyl
```

## Quick Start

UPDATING ... 

```
import pandas as pd
from mssibyl.models import BaseModel
from mssibyl.trainer import BaseTrainer
from mssibyl.datasets.utils import make_timeline

df = pd.read_csv("./data.csv")
df.columns = ["ds", "y"]

df_with_future = make_timeline(df, freq="H", periods=300)

args = {
    "lookback": 48,
    "forecast_horizon": 1,
    "data": df_with_future,
    "target": "y",
}

sibyl_model = BaseModel(args)
trainer = BaseTrainer(
    max_epochs=100, gpus=1, show_progress_bar=False, early_stop_callback=True
)
trainer.fit(sibyl_model)
preds = sibyl_model.predict(interval=True, include_history=True)

preds.to_csv("res.csv", index=False)
```

## Changelog


### Version 0.1 (coming)

- Initial release

## License

Sibyl is licensed under the [MIT license](LICENSE.md)