Metadata-Version: 2.1
Name: PipelineTS
Version: 0.2.3
Summary: One-stop time series analysis tool, supporting time series data preprocessing, feature engineering, model training, model evaluation, and model prediction.
Home-page: https://github.com/BirchKwok/PipelineTS
Author: Birch Kwok
Author-email: birchkwok@gmail.com
Keywords: timeseries machine learning
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: numpy>=1.24.3
Requires-Dist: pandas>=2.0.3
Requires-Dist: matplotlib>=3.7.1
Requires-Dist: frozendict>=2.3.0
Requires-Dist: darts>=0.24.0
Requires-Dist: prophet>=1.1.4
Requires-Dist: spinesTS>=0.3.7
Requires-Dist: spinesUtils>=0.3.5
Requires-Dist: lightgbm>=3.3.5
Requires-Dist: IPython>=8.12.1
Requires-Dist: tabulate>=0.8.9
Requires-Dist: torch>=2.1.0
Requires-Dist: xgboost>=2.0.0
Requires-Dist: catboost>=1.2.2

# PipelineTS

一站式时间序列分析工具，支持时序数据预处理、特征工程、模型训练、模型评估、模型预测等。

## 安装

```bash
conda install -c conda-forge prophet

python -m pip install PipelineTS
```

## 快速开始

```python
from PipelineTS.dataset import LoadWebSales
init_data = LoadWebSales()[['date', 'type_a']]

valid_data = init_data.iloc[-30:, :]
data = init_data.iloc[:-30, :]

from PipelineTS.pipeline import PipelineTS
# list all models
PipelineTS.list_models()

from sklearn.metrics import mean_absolute_error
pipeline = PipelineTS(
    time_col='date', 
    target_col='type_a', 
    lags=30, 
    random_state=42, 
    metric=mean_absolute_error, 
    metric_less_is_better=True
)

# training all models
pipeline.fit(data, valid_df=valid_data)

# use best model to predict next 30 steps data point
res = pipeline.predict(30)

```

### 数据准备

```python
# TODO
```

### 预处理

```python
# TODO
```

### 特征工程

```python
# TODO
```

### 模型训练

```python
# TODO
```

### 模型评估    

```python
# TODO
```

### 模型预测

```python
# TODO
```

### 模型部署

```python
# TODO
```
