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七个最新的时间序列分析库介绍和代码示例

2023-04-10  微信公众号  DeepHub IMBA
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时间序列分析包括检查随着时间推移收集的数据点,目的是确定可以为未来预测提供信息的模式和趋势。我们已经介绍过很多个时间序列分析库了,但是随着时间推移,新的库和更新也在不断的出现,所以本文将分享8个目前比较常用的,用于处理时间序列问题的Python/ target=_blank class=infotextkey>Python库。他们是tsfresh, autots, darts, atspy, kats, sktime, greykite。

1、Tsfresh

Tsfresh在时间序列特征提取和选择方面功能强大。它旨在自动从时间序列数据中提取大量特征,并识别出最相关的特征。Tsfresh支持多种时间序列格式,可用于分类、聚类和回归等各种应用程序。

 
import pandas as pd
 from tsfresh import extract_features
 from tsfresh.utilities.dataframe_functions import make_forecasting_frame
 
 # Assume we have a time series dataset `data` with columns "time" and "value"
 data = pd.read_csv('data.csv')
 
 # We will use the last 10 points to predict the next point
 df_shift, y = make_forecasting_frame(data["value"], kind="value", max_timeshift=10, rolling_direction=1)
 
 # Extract relevant features using tsfresh
 X = extract_features(df_shift, column_id="id", column_sort="time", column_value="value", impute_function=impute)

2、AutoTS

autots是另一个用于时间序列预测的Python库:

 
from autots.datasets import load_monthly
 
 df_long = load_monthly(long=True)
 
 from autots import AutoTS
 
 model = AutoTS(
     forecast_length=3,
     frequency='infer',
     ensemble='simple',
     max_generations=5,
     num_validations=2,
 )
 model = model.fit(df_long, date_col='datetime', value_col='value', id_col='series_id')
 
 # Print the description of the best model
 print(model)

3、darts

darts(Data Analytics and Real-Time Systems)有多种时间序列预测模型,包括ARIMA、Prophet、指数平滑的各种变体,以及各种深度学习模型,如LSTMs、gru和tcn。Darts还具有用于交叉验证、超参数调优和特征工程的内置方法。

darts的一个关键特征是能够进行概率预测。这意味着,不仅可以为每个时间步骤生成单点预测,还可以生成可能结果的分布,从而更全面地理解预测中的不确定性。

 
import pandas as pd
 import matplotlib.pyplot as plt
 
 from darts import TimeSeries
 from darts.models import ExponentialSmoothing
 
 # Read data
 df = pd.read_csv("AirPassengers.csv", delimiter=",")
 
 # Create a TimeSeries, specifying the time and value columns
 series = TimeSeries.from_dataframe(df, "Month", "#Passengers")
 
 # Set aside the last 36 months as a validation series
 train, val = series[:-36], series[-36:]
 
 # Fit an exponential smoothing model, and make a (probabilistic)
 # prediction over the validation series’ duration
 model = ExponentialSmoothing()
 model.fit(train)
 prediction = model.predict(len(val), num_samples=1000)
 
 # Plot the median, 5th and 95th percentiles
 series.plot()
 prediction.plot(label="forecast", low_quantile=0.05, high_quantile=0.95)
 plt.legend()

图片

4、AtsPy

atspy,可以简单地加载数据并指定要测试的模型,如下面的代码所示。

 
# Importing packages
 import pandas as pd
 from atspy import AutomatedModel
 
 # Reading data
 df = pd.read_csv("AirPassengers.csv", delimiter=",")
 
 # Preprocessing data
 data.columns = ['month','Passengers']
 data['month'] = pd.to_datetime(data['month'],infer_datetime_format=True,format='%y%m')
 data.index = data.month
 df_air = data.drop(['month'], axis = 1)
 
 # Select the models you want to run:
 models = ['ARIMA','Prophet']
 run_models = AutomatedModel(df = df_air, model_list=models, forecast_len=10)

该包提供了一组完全自动化的模型。包括:

 

图片

 

5、kats

kats (kit to Analyze Time Series)是一个由Facebook(现在的Meta)开发的Python库。这个库的三个核心特性是:

模型预测:提供了一套完整的预测工具,包括10+个单独的预测模型、集成、元学习模型、回溯测试、超参数调优和经验预测区间。

检测:Kats支持检测时间序列数据中的各种模式的函数,包括季节性、异常、变化点和缓慢的趋势变化。

特征提取和嵌入:Kats中的时间序列特征(TSFeature)提取模块可以生成65个具有明确统计定义的特征,可应用于大多数机器学习(ML)模型,如分类和回归。

 
# pip install kats
 
 import pandas as pd
 from kats.consts import TimeSeriesData
 from kats.models.prophet import ProphetModel, ProphetParams
 
 # Read data
 df = pd.read_csv("AirPassengers.csv", names=["time", "passengers"])
 
 # Convert to TimeSeriesData object
 air_passengers_ts = TimeSeriesData(air_passengers_df)
 
 # Create a model param instance
 params = ProphetParams(seasonality_mode='multiplicative')
 
 # Create a prophet model instance
 m = ProphetModel(air_passengers_ts, params)
 
 # Fit model simply by calling m.fit()
 m.fit()
 
 # Make prediction for next 30 month
 forecast = m.predict(steps=30, freq="MS")
 forecast.head()

6、Sktime

sktime是一个用于时间序列分析的库,它构建在scikit-learn之上,并遵循类似的API,可以轻松地在两个库之间切换。下面是如何使用Sktime进行时间序列分类的示例:

from sktime.datasets import load_arrow_head
 from sktime.classification.compose import TimeSeriesForestClassifier
 from sktime.utils.sampling import train_test_split
 
 # Load ArrowHead dataset
 X, y = load_arrow_head(return_X_y=True)
 
 # Split data into train and test sets
 X_train, X_test, y_train, y_test = train_test_split(X, y)
 
 # Create and fit a time series forest classifier
 classifier = TimeSeriesForestClassifier(n_estimators=100)
 classifier.fit(X_train, y_train)
 
 # Predict labels for the test set
 y_pred = classifier.predict(X_test)
 
 # Print classification report
 from sklearn.metrics import classification_report
 print(classification_report(y_test, y_pred))

7、GreyKite

greykite是LinkedIn发布的一个时间序列预测库。该库可以处理复杂的时间序列数据,并提供一系列功能,包括自动化特征工程、探索性数据分析、预测管道和模型调优。

from greykite.common.data_loader import DataLoader
 from greykite.framework.templates.autogen.forecast_config import ForecastConfig
 from greykite.framework.templates.autogen.forecast_config import MetadataParam
 from greykite.framework.templates.forecaster import Forecaster
 from greykite.framework.templates.model_templates import ModelTemplateEnum
 
 # Defines inputs
 df = DataLoader().load_bikesharing().tail(24*90)  # Input time series (pandas.DataFrame)
 config = ForecastConfig(
      metadata_param=MetadataParam(time_col="ts", value_col="count"),  # Column names in `df`
      model_template=ModelTemplateEnum.AUTO.name,  # AUTO model configuration
      forecast_horizon=24,   # Forecasts 24 steps ahead
      coverage=0.95,         # 95% prediction intervals
  )
 
 # Creates forecasts
 forecaster = Forecaster()
 result = forecaster.run_forecast_config(df=df, config=config)
 
 # Accesses results
 result.forecast     # Forecast with metrics, diagnostics
 result.backtest     # Backtest with metrics, diagnostics
 result.grid_search  # Time series CV result
 result.model        # Trained model
 result.timeseries   # Processed time series with plotting functions

总结

我们可以看到,这些时间序列的库主要功能有2个方向,一个是特征的生成,另外一个就是多种时间序列预测模型的集成,所以无论是处理单变量还是多变量数据,它们都可以满足我们的需求,但是具体用那个还要看具体的需求和使用的习惯。

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