工夫序列剖析包含查看随着时间推移收集的数据点，目标是确定能够为将来预测提供信息的模式和趋势。咱们曾经介绍过很多个工夫序列剖析库了，然而随着时间推移，新的库和更新也在一直的呈现，所以本文将分享8个目前比拟罕用的，用于解决工夫序列问题的Python库。他们是tsfresh, autots, darts, atspy, kats, sktime, greykite。
1、Tsfresh
Tsfresh在工夫序列特征提取和抉择方面功能强大。它旨在主动从工夫序列数据中提取大量特色，并辨认出最相干的特色。Tsfresh反对多种工夫序列格局，可用于分类、聚类和回归等各种应用程序。
importpandasaspd fromtsfreshimportextract_features fromtsfresh.utilities.dataframe_functionsimportmake_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是另一个用于工夫序列预测的Python库：
- 提供了单变量和多变量工夫序列预测的各种算法，包含ARIMA, ETS, Prophet和DeepAR。
- 为最佳模型执行主动模型集成。
- 提供了上界和下界的置信区间预测。
- 通过学习最优NaN imputation和异样值去除来解决数据。
fromautots.datasetsimportload_monthly df_long=load_monthly(long=True) fromautotsimportAutoTS 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)有多种工夫序列预测模型，包含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还具备用于穿插验证、超参数调优和特色工程的内置办法。
darts的一个要害特色是可能进行概率预测。这意味着，不仅能够为每个工夫步骤生成单点预测，还能够生成可能后果的散布，从而更全面地了解预测中的不确定性。
importpandasaspd importmatplotlib.pyplotasplt fromdartsimportTimeSeries fromdarts.modelsimportExponentialSmoothing # 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()4、AtsPy
atspy，能够简略地加载数据并指定要测试的模型，如上面的代码所示。
# Importing packages importpandasaspd fromatspyimportAutomatedModel # 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)该包提供了一组齐全自动化的模型。包含:
5、kats
kats (kit to Analyze Time Series)是一个由Facebook(当初的Meta)开发的Python库。这个库的三个外围个性是:
模型预测:提供了一套残缺的预测工具，包含10+个独自的预测模型、集成、元学习模型、回溯测试、超参数调优和教训预测区间。
检测:Kats反对检测时间序列数据中的各种模式的函数，包含季节性、异样、变动点和迟缓的趋势变动。
特征提取和嵌入:Kats中的工夫序列特色(TSFeature)提取模块能够生成65个具备明确统计定义的特色，可利用于大多数机器学习(ML)模型，如分类和回归。
# pip install kats importpandasaspd fromkats.constsimportTimeSeriesData fromkats.models.prophetimportProphetModel, 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是一个用于工夫序列剖析的库，它构建在scikit-learn之上，并遵循相似的API，能够轻松地在两个库之间切换。上面是如何应用Sktime进行工夫序列分类的示例:
fromsktime.datasetsimportload_arrow_head fromsktime.classification.composeimportTimeSeriesForestClassifier fromsktime.utils.samplingimporttrain_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 fromsklearn.metricsimportclassification_report print(classification_report(y_test, y_pred))7、GreyKite
greykite是LinkedIn公布的一个工夫序列预测库。该库能够解决简单的工夫序列数据，并提供一系列性能，包含自动化特色工程、探索性数据分析、预测管道和模型调优。
fromgreykite.common.data_loaderimportDataLoader fromgreykite.framework.templates.autogen.forecast_configimportForecastConfig fromgreykite.framework.templates.autogen.forecast_configimportMetadataParam fromgreykite.framework.templates.forecasterimportForecaster fromgreykite.framework.templates.model_templatesimportModelTemplateEnum # 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
咱们能够看到，这些工夫序列的库次要性能有2个方向，一个是特色的生成，另外一个就是多种工夫序列预测模型的集成，所以无论是解决单变量还是多变量数据，它们都能够满足咱们的需要，然而具体用那个还要看具体的需要和应用的习惯。
https://avoid.overfit.cn/post/45451d119a154aeba72bf8dd3eaa9496
作者：Joanna