Darts 是一个 Python 库，用于对时间序列进行用户友好的预测和异常检测。它包含多种模型，从经典的 ARIMA 到深度神经网络。所有预测模型都可以以相同的方式使用，通过 fit() 和 predict() 函数，类似于 scikit-learn。该库还使回测模型、组合多个模型的预测以及考虑外部数据变得容易。Darts 同时支持单变量和多变量的时间序列与模型。基于机器学习的模型可以在包含多个时间序列的大型数据集上训练，并且其中一些模型为概率预测提供了丰富的支持。
Darts 还提供了广泛的异常检测能力。例如，将 PyOD 模型应用于时间序列以获得异常分数非常简便，或者包装任何 Darts 预测或滤波模型，以获得功能完整的异常检测模型。
我们建议首先使用你喜欢的工具为你的项目建立一个干净的 Python 3.10+ 环境 （conda、 venv、virtualenv 或不用 virtualenvwrapper）。
一旦环境设置完毕，你就可以使用 pip 安装 darts：
pip install darts
更多细节请参阅我们的 安装说明。
根据 Pandas DataFrame 创建一个 TimeSeries 对象，并将其拆分为训练/验证序列：```python
import pandas as pd
from darts import TimeSeries
df = pd.read_csv("AirPassengers.csv", delimiter=",")
series = TimeSeries.from_dataframe(df, "Month", "#Passengers")
train, val = series[:-36], series[-36:]
拟合一个指数平滑模型，并对验证序列的持续时间进行（概率性）预测：```python
from darts.models import ExponentialSmoothing
model = ExponentialSmoothing()
model.fit(train)
prediction = model.predict(len(val), num_samples=1000)
绘制中位数、第5和第95百分位数:```python import matplotlib.pyplot as plt
series.plot() prediction.plot(label="forecast", low_quantile=0.05, high_quantile=0.95) plt.legend()
<div style="text-align:center;">
<img src="https://raw.githubusercontent.com/unit8co/darts/master/static/images/example.png" alt="darts 预测示例" />
</div>
### 异常检测
加载多变量序列，对其进行修剪，保留 2 个分量，划分训练集和验证集：```python
from darts.datasets import ETTh2Dataset
series = ETTh2Dataset().load()[:10000][["MUFL", "LULL"]]
train, val = series.split_before(0.6)
构建一个 k-means 异常评分器，在训练集上训练它 并在验证集上使用它来获得异常分数：```python from darts.ad import KMeansScorer
scorer = KMeansScorer(k=2, window=5) scorer.fit(train) anom_score = scorer.score(val)
构建一个二分类异常检测器，在训练分数上训练它，然后在验证分数上使用它来获得二分类异常分类：```python
from darts.ad import QuantileDetector
detector = QuantileDetector(high_quantile=0.99)
detector.fit(scorer.score(train))
binary_anom = detector.detect(anom_score)
绘图（平移和缩放某些序列，以使所有内容显示在同一幅图上）：```python import matplotlib.pyplot as plt
series.plot() (anom_score / 2. - 100).plot(label="computed anomaly score", c="orangered", lw=3) (binary_anom * 45 - 150).plot(label="detected binary anomaly", lw=4)
<div style="text-align:center;">
<img src="https://raw.githubusercontent.com/unit8co/darts/master/static/images/example_ad.png" alt="darts anomaly detection example" />
</div>
## 功能特性
* **预测模型：** 大量用于回归和分类任务的预测模型；从统计模型（如
ARIMA）到深度学习模型（如 N-BEATS）。请参阅下面的[预测模型](#forecasting-models)。
* **异常检测** `darts.ad` 模块包含一系列异常评分器、
检测器和聚合器，它们可以组合起来检测时间序列中的异常。
可以轻松地将 Darts 的任何预测或过滤模型包装起来，构建
一个将预测与实际值进行比较的完整异常检测模型。
`PyODScorer` 使得在时间序列上使用 PyOD 检测器变得非常简单。
* **多变量支持：** `TimeSeries` 可以是多变量的，即包含多个随时间变化的
维度/列，而不是单个标量值。许多模型可以接受并生成多变量序列。
* **多序列训练（全局模型）：** 所有基于机器学习的模型（包括所有神经网络）
都支持在多个（可能是多变量的）序列上进行训练。这也可以扩展到大型数据集。
* **概率支持：** `TimeSeries` 对象可以（可选地）表示随机
时间序列；例如，这可用于获取置信区间，并且许多模型支持不同
风格的概率预测（例如估计参数分布或分位数）。
某些异常检测评分器也能够利用这些预测分布。
* **共形预测支持：** 我们的共形预测模型允许为任何预训练的全局预测模型生成具有
校准分位数区间的概率预测。
* **过去和未来协变量支持：** Darts 中的许多模型支持将过去观测到的和/或未来已知的
协变量（外部数据）时间序列作为输入来生成预测。
* **静态协变量支持：** 除了随时间变化的数据外，`TimeSeries` 还可以包含
每个维度的静态数据，某些模型可以利用这些数据。
* **层级调和：** Darts 提供执行调和（reconciliation）的变换器。
这些变换器可以使预测以尊重底层层级结构的方式相加。
* **回归模型：** 可以插入任何与 scikit-learn 兼容的模型，
以将预测作为目标序列和协变量的滞后值的函数来获取。
* **使用样本权重训练：** 所有全局模型都支持使用样本权重进行训练。它们可以
应用于每个观测值、预测时间步和目标列。
* **预测起点偏移：** 所有全局模型都支持在偏移的输出窗口上进行训练和预测。
例如，这对于日前市场（Day-Ahead Market）预测，或当协变量（或目标序列）延迟
上报时非常有用。
* **可解释性：** Darts 能够使用 SHAP 值来*解释*某些预测模型。
* **数据处理：** 可轻松对时间序列数据应用（和还原）常见变换的工具
（缩放、填充缺失值、差分、Box-Cox 变换等）。
* **评估指标：** 用于评估时间序列拟合优度的各种指标；
从 R2 分数到平均绝对缩放误差（Mean Absolute Scaled Error）。
* **回测：** 使用移动时间窗口模拟历史预测的工具。
* **PyTorch Lightning 支持：** 所有深度学习模型均使用 PyTorch Lightning 实现，
支持自定义回调、GPU/TPU 训练和自定义训练器等。
* **过滤模型：** Darts 提供三种过滤模型：`KalmanFilter`、`GaussianProcessFilter`，
以及 `MovingAverageFilter`，它们可以对时间序列进行过滤，并在某些情况下获得
底层状态/值的概率推断。
* **数据集** `darts.datasets` 子模块包含一些常用时间序列数据集，用于快速
且可重复的实验。
* **多后端兼容性：** `TimeSeries` 对象可以从各种后端创建并导出到各种后端，例如 pandas、polars、numpy、pyarrow、xarray 等，促进与不同数据处理库的无缝集成。
## 预测模型
以下是 Darts 当前实现的预测模型的细分。我们的套件包括回归模型和分类模型，每种模型都针对特定的预测任务而定制。我们致力于不断扩展新的模型和功能，以增强您的预测能力。
**回归模型：** 我们的回归模型旨在预测连续的数值，非常适合预测时间序列数据中的未来趋势和模式。利用这些模型，您可以基于历史数据洞察潜在的未来结果。| 模型 | 来源 | 目标序列支持：<br/><br/>单变量/<br/>多变量 | 协变量支持：<br/><br/>过去观测/<br/>未来已知/<br/>静态 | 概率预测：<br/><br/>采样/<br/>分布参数 | 多序列训练与预测 |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------------------|--------------------------------------------------------------------------|-------------------------------------------|
| **基线模型**<br/>([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | |
| [NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 |
| [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 |
| [NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 |
| [NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 |
| **统计 / 经典模型**<br/>([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | |
| [ARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA) | | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 |
| [VARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA) | | 🔴 ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 |
| [ExponentialSmoothing](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing) | | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 |
| [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) 和 [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | [Theta 论文](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta 源码](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 |
| [Prophet](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet) | [Prophet 仓库](https://github.com/facebook/prophet) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 |
| [FFT](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT)（快速傅里叶变换） | | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 |
| [KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) 使用卡尔曼滤波器和 N4SID 进行系统辨识 | [N4SID 论文](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 |
| [TBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_tbats.html#darts.models.forecasting.sf_tbats.TBATS) | [TBATS 论文](https://robjhyndman.com/papers/ComplexSeasonality.pdf) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [Croston](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_croston.html#darts.models.forecasting.sf_croston.Croston) 方法 | | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [StatsForecastModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_model.html#darts.models.forecasting.sf_model.StatsForecastModel) 任意 [StatsForecast](https://nixtlaverse.nixtla.io/statsforecast/index.html#models) 模型的封装器 | [Nixtla 的 statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [AutoARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.AutoARIMA) | [Nixtla 的 statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [AutoETS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.AutoETS) | [Nixtla 的 statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [AutoCES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.AutoCES) | [Nixtla 的 statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [AutoMFLES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_mfles.html#darts.models.forecasting.sf_auto_mfles.AutoMFLES) | [Nixtla 的 statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [AutoTBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_tbats.html#darts.models.forecasting.sf_auto_tbats.AutoTBATS) | [Nixtla 的 statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [AutoTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.AutoTheta) | [Nixtla 的 statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| [MultivariateModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.multivariate_model.html#darts.models.forecasting.multivariate_model.MultivariateModel) | | ✅ ✅ | 🔴 ✅ 🔴 | ✅ ✅ | 🔴 |
| **全局基线模型**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | |
| [GlobalNaiveAggregate](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ |
| [GlobalNaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ |
| [GlobalNaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ |
| **回归模型**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | |
| [SKLearnModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sklearn_model.html#darts.models.forecasting.sklearn_model.SKLearnModel)：任意类 scikit-learn 回归模型的封装器 | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ |
| [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [RandomForestModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForestModel) | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ |
| [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| **基于 PyTorch (Lightning) 的模型**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | |
| [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel)（包括 LSTM 和 GRU）；其概率版本等同于 DeepAR | [DeepAR 论文](https://arxiv.org/abs/1704.04110) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ ✅ | ✅ |
| [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel)（包括 LSTM 和 GRU） | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | [N-BEATS 论文](https://arxiv.org/abs/1905.10437) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ |
| [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | [N-HiTS 论文](https://arxiv.org/abs/2201.12886) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ |
| [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | [TCN 论文](https://arxiv.org/abs/1803.01271)，[DeepTCN 论文](https://arxiv.org/abs/1906.04397)，[博客文章](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ |
| [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ |
| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel)（时间融合 Transformer） | [TFT 论文](https://arxiv.org/pdf/1912.09363.pdf)，[PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | [DLinear 论文](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | [NLinear 论文](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | [TiDE 论文](https://arxiv.org/pdf/2304.08424.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [TSMixerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel) | [TSMixer 论文](https://arxiv.org/pdf/2303.06053.pdf)，[PyTorch 实现](https://github.com/ditschuk/pytorch-tsmixer) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [NeuralForecastModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nf_model.html#darts.models.forecasting.nf_model.NeuralForecastModel)：任意 [NeuralForecast](https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html) 基础模型的封装器 | [NeuralForecast 文档](https://nixtlaverse.nixtla.io/neuralforecast/docs/) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| **基础模型**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms))：无需训练 | | | | | |
| [Chronos2Model](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.chronos2_model.html#darts.models.forecasting.chronos2_model.Chronos2Model) | [Chronos-2 报告](https://arxiv.org/abs/2510.15821)，[Amazon 博客文章](https://www.amazon.science/blog/introducing-chronos-2-from-univariate-to-universal-forecasting) | ✅ ✅ | ✅ ✅ 🔴 | ✅ ✅ | ✅ |
| [TimesFM2p5Model](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.timesfm2p5_model.html#darts.models.forecasting.timesfm2p5_model.TimesFM2p5Model) | [TimesFM 1.0 论文](https://arxiv.org/abs/2310.10688)，[Google 博客文章](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting) | ✅ ✅ | 🔴 🔴 🔴 | ✅ ✅ | ✅ |
| [TiRexModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tirex_model.html#darts.models.forecasting.tirex_model.TiRexModel) | [TiRex 论文](https://arxiv.org/abs/2505.23719)，[TiRex GitHub](https://github.com/NX-AI/tirex) | ✅ ✅ | 🔴 🔴 🔴 | ✅ ✅ | ✅ |
| [PatchTSTFMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.patchtst_fm_model.html#darts.models.forecasting.patchtst_fm_model.PatchTSTFMModel) | [PatchTST-FM 论文](https://arxiv.org/abs/2602.06909)，[PatchTST-FM GitHub](https://github.com/ibm-granite/granite-tsfm) | ✅ ✅ | 🔴 🔴 🔴 | ✅ ✅ | ✅ |
| **集成模型**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms))：模型支持取决于所集成的预测模型及集成模型本身 | | | | | |
| [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.naive_ensemble_model.html#darts.models.forecasting.naive_ensemble_model.NaiveEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| **保形模型**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms))：模型支持取决于所使用的预测模型 | | | | | |
| [ConformalNaiveModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel) | [保形预测](https://arxiv.org/pdf/1905.03222) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [ConformalQRModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel) | [保形分位数回归](https://arxiv.org/pdf/1905.03222) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |**分类模型：** Darts 中的分类模型旨在预测分类类别标签，从而实现有效的时间序列标注和未来类别预测。这些模型非常适合需要识别随时间变化的不同类别或状态的场景。
| Model | 来源 | 目标序列支持：<br/><br/>单变量/<br/>多变量 | 协变量支持：<br/><br/>过去观测/<br/>未来已知/<br/>静态 | 概率预测：<br/><br/>采样/<br/>分布参数 | 多序列上的训练与预测 |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|--------------------------------------------------------------|--------------------------------------------------------------------------|--------------------------------------------------------------------------|-------------------------------------------|
| **回归模型**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | |
| [SKLearnClassifierModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sklearn_model.html#darts.models.forecasting.sklearn_model.SKLearnClassifierModel): 围绕任何类似 scikit-learn 的分类模型的包装器 | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [CatBoostClassifierModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostClassifierModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [LightGBMClassifierModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMClassifierModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [XGBClassifierModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBClassifierModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
## 社区与联系
欢迎任何人加入我们的 [Gitter 聊天室](https://gitter.im/u8darts/darts) 来提问、提出建议、
讨论用例等。如果您发现错误或有建议，也欢迎在 GitHub 上提交 issues。
如果您想告知我们的内容不适合在 Gitter 或 Github 上讨论，
欢迎发送电子邮件至 <a href="mailto:[email protected]">[email protected]</a> 处理与 darts 相关的事宜，或发送至 <a href="mailto:[email protected]">[email protected]</a> 处理其他任何咨询。
## 贡献
开发工作持续进行中，我们欢迎在 GitHub 上提交建议、拉取请求和 issues。
所有贡献者都将在
[变更日志页面](https://github.com/unit8co/darts/blob/master/CHANGELOG.md)上获得致谢。
在着手进行贡献（新功能或修复）之前，
请[查看我们的贡献指南](https://github.com/unit8co/darts/blob/master/CONTRIBUTING.md)。
## 引用
如果您在科研工作中使用 Darts，我们希望您引用以下 JMLR 论文。
[Darts: User-Friendly Modern Machine Learning for Time Series](https://www.jmlr.org/papers/v23/21-1177.html)
Bibtex 条目（或直接通过 GitHub 的仓库引用功能复制）：```
@article{Herzen_Darts_User-Friendly_Modern_2022,
author = {Herzen, Julien and Lässig, Francesco and Piazzetta, Samuele Giuliano and Neuer, Thomas and Tafti, Léo and Raille, Guillaume and Van Pottelbergh, Tomas and Pasieka, Marek and Skrodzki, Andrzej and Huguenin, Nicolas and Dumonal, Maxime and Kościsz, Jan and Bader, Dennis and Gusset, Frédérick and Benheddi, Mounir and Williamson, Camila and Kosinski, Michal and Petrik, Matej and Grosch, Gaël},
journal = {Journal of Machine Learning Research},
number = {124},
pages = {1--6},
title = {{Darts: User-Friendly Modern Machine Learning for Time Series}},
url = {https://jmlr.org/papers/v23/21-1177.html},
volume = {23},
year = {2022}
}