AutoTS

AutoTS 是一个 Python 时间序列包，旨在大规模快速部署高精度预测。

2023 年，AutoTS 在 M6 预测竞赛中获胜，在 12 个月的股市预测中提供了最高绩效的投资决策。

有数十种预测模型可用于sklearn风格的.fit()和.predict()。 其中包括朴素、统计、机器学习和深度学习模型。 此外，在sklearn风格的.fit()、.transform()和.inverse_transform()中，还有超过 30 种特定于时间序列的变换。 所有这些功能都直接在 Pandas Dataframes 上运行，无需转换为专有对象。

所有模型都支持预测多元（多个时间序列）输出，并且还支持概率（上限/下限）预测。 大多数模型可以轻松扩展到数万甚至数十万个输入系列。 许多模型还支持传入用户定义的外生回归量。

这些模型均设计用于集成到 AutoML 特征搜索中，该搜索可通过遗传算法自动查找给定数据集的最佳模型、预处理和集成。

水平(Horizontal)和马赛克风格(mosaic style)的组合是旗舰组合类型，允许每个系列接收最准确的模型，同时仍然保持可扩展性。

指标和交叉验证选项的组合、应用子集和加权的能力、回归器生成工具、模拟预测模式、事件风险预测、实时数据集、模板导入和导出、绘图以及数据整形参数的集合使 可用的功能集。

目录

• 安装
• 基本使用
• 速度和大数据提示
• 扩展教程 GitHub or Docs
• 生产示例

安装

pip install autots

这包括基本模型的依赖项，但某些模型和方法需要附加包

请注意，还有其他几个项目选择了类似的名称，因此请确保您使用的是正确的 AutoTS 代码、论文和文档。.

基本使用

AutoTS 的输入数据预计采用 长 或 宽 格式： （long or wide ）

• wide 格式是一个带有pandas.DatetimeIndex的pandas.DataFrame，每列都是一个不同的series。
• long 格式包含三列：
  • Date（最好已经是 pandas 识别的 日期时间 格式）。
  • Series ID. 对于单个时间序列，series_id 可以 = None。
  • Value
• 对于 long 数据，每个数据的列名称都会作为date_col、id_col和value_col传递给``.fit()`。 wide 数据不需要参数。

Lower-level 的函数仅针对 wide类型数据而设计。

# 其他载入选项: _hourly, _monthly, _weekly, _yearly, or _live_daily
from autots import AutoTS, load_daily

# 示例数据集可用于*长*导入形状或*宽*导入形状
long = False
df = load_daily(long=long)

model = AutoTS(
    forecast_length=21,
    frequency="infer",
    prediction_interval=0.9,
    ensemble=None,
    model_list="superfast",  # "fast", "default", "fast_parallel"
    transformer_list="fast",  # "superfast",
    drop_most_recent=1,
    max_generations=4,
    num_validations=2,
    validation_method="backwards"
)
model = model.fit(
    df,
    date_col='datetime' if long else None,
    value_col='value' if long else None,
    id_col='series_id' if long else None,
)

prediction = model.predict()
# 绘制一个样本
prediction.plot(model.df_wide_numeric,
                series=model.df_wide_numeric.columns[0],
                start_date="2019-01-01")
# 打印最佳模型的详细信息
print(model)

# 点预测 dataframe
forecasts_df = prediction.forecast
# 预测上限和下限
forecasts_up, forecasts_low = prediction.upper_forecast, prediction.lower_forecast

# 所有尝试的模型结果的准确性
model_results = model.results()
# 并从交叉验证中汇总
validation_results = model.results("validation")

The lower-level API, in particular the large section of time series transformers in the scikit-learn style, can also be utilized independently from the AutoML framework.

Check out extended_tutorial.md for a more detailed guide to features.

Also take a look at the production_example.py

Tips for Speed and Large Data:

• Use appropriate model lists, especially the predefined lists:
  • superfast (simple naive models) and fast (more complex but still faster models, optimized for many series)
  • fast_parallel (a combination of fast and parallel) or parallel, given many CPU cores are available
    • n_jobs usually gets pretty close with ='auto' but adjust as necessary for the environment
  • 'scalable' is the best list to avoid crashing when many series are present. There is also a transformer_list = 'scalable'
  • see a dict of predefined lists (some defined for internal use) with from autots.models.model_list import model_lists
• Use the subset parameter when there are many similar series, subset=100 will often generalize well for tens of thousands of similar series.
  • if using subset, passing weights for series will weight subset selection towards higher priority series.
  • if limited by RAM, it can be distributed by running multiple instances of AutoTS on different batches of data, having first imported a template pretrained as a starting point for all.
• Set model_interrupt=True which passes over the current model when a KeyboardInterrupt ie crtl+c is pressed (although if the interrupt falls between generations it will stop the entire training).
• Use the result_file method of .fit() which will save progress after each generation - helpful to save progress if a long training is being done. Use import_results to recover.
• While Transformations are pretty fast, setting transformer_max_depth to a lower number (say, 2) will increase speed. Also utilize transformer_list == 'fast' or 'superfast'.
• Check out this example of using AutoTS with pandas UDF.
• Ensembles are obviously slower to predict because they run many models, 'distance' models 2x slower, and 'simple' models 3x-5x slower.
  • ensemble='horizontal-max' with model_list='no_shared_fast' can scale relatively well given many cpu cores because each model is only run on the series it is needed for.
• Reducing num_validations and models_to_validate will decrease runtime but may lead to poorer model selections.
• For datasets with many records, upsampling (for example, from daily to monthly frequency forecasts) can reduce training time if appropriate.
  • this can be done by adjusting frequency and aggfunc but is probably best done before passing data into AutoTS.
• It will be faster if NaN's are already filled. If a search for optimal NaN fill method is not required, then fill any NaN with a satisfactory method before passing to class.
• Set runtime_weighting in metric_weighting to a higher value. This will guide the search towards faster models, although it may come at the expense of accuracy.
• Memory shortage is the most common cause of random process/kernel crashes. Try testing a data subset and using a different model list if issues occur. Please also report crashes if found to be linked to a specific set of model parameters (not AutoTS parameters but the underlying forecasting model params). Also crashes vary significantly by setup such as underlying linpack/blas so seeing crash differences between environments can be expected.

How to Contribute:

• Give feedback on where you find the documentation confusing
• Use AutoTS and...
  • Report errors and request features by adding Issues on GitHub
  • Posting the top model templates for your data (to help improve the starting templates)
  • Feel free to recommend different search grid parameters for your favorite models
• And, of course, contributing to the codebase directly on GitHub.

AutoTS Process

flowchart TD
    A[Initiate AutoTS Model] --> B[Import Template]
    B --> C[Load Data]
    C --> D[Split Data Into Initial Train/Test Holdout]
    D --> E[Run Initial Template Models]
    E --> F[Evaluate Accuracy Metrics on Results]
    F --> G[Generate Score from Accuracy Metrics]
    G --> H{Max Generations Reached or Timeout?}

    H -->|No| I[Evaluate All Previous Templates]
    I --> J[Genetic Algorithm Combines Best Results and New Random Parameters into New Template]
    J --> K[Run New Template Models and Evaluate]
    K --> G

    H -->|Yes| L[Select Best Models by Score for Validation Template]
    L --> M[Run Validation Template on Additional Holdouts]
    M --> N[Evaluate and Score Validation Results]
    N --> O{Create Ensembles?}
    
    O -->|Yes| P[Generate Ensembles from Validation Results]
    P --> Q[Run Ensembles Through Validation]
    Q --> N

    O -->|No| R[Export Best Models Template]
    R --> S[Select Single Best Model]
    S --> T[Generate Future Time Forecast]
    T --> U[Visualize Results]

    R --> B[Import Best Models Template]

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Also known as Project CATS (Catlin's Automated Time Series) hence the logo.