七个最新的时间序列分析库介绍和代码示例

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)

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)

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()

# 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)

# 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()

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))

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