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时间序列预测的7种Python工具包,总有一款适合你

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时间序列问题是数据科学中最难解决的问题之一。传统的处理方法如 ARIMA、SARIMA 等,虽然是很好,但在处理具有非线性特性或非平稳时间序列问题时很难取得满意的预测效果。

为了获得更好的预测效果,并且可以简单高效的完成任务,本文中我将分享给大家7个用于处理时间序列问题的 Python 工具包,有所收获,点赞支持,欢迎收藏学习。

1、tsfresh

tsfresh 是一个很棒的 python 包,它可以自动计算大量的时间序列特性,包含许多特征提取方法和强大的特征选择算法。

让我们以获取航空公司乘客的标准数据集为例,来了解tsfresh

# Importing libraries
import pandas as pd
from tsfresh import extract_features, extract_relevant_features, select_features
from tsfresh.utilities.dataframe_functions import impute, make_forecasting_frame
from tsfresh.feature_extraction import ComprehensiveFCParameters, settings

# Reading the data
data = pd.read_csv('../input/air-passengers/AirPassengers.csv')

# Some preprocessing for time component:
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)

# Use Forecasting frame from tsfresh for rolling forecast training
df_shift, y_air = make_forecasting_frame(df_air["Passengers"], kind="Passengers", max_timeshift=12, rolling_direction=1)
print(df_shift)

 

数据需要被格式化为如下格式:

# Getting Comprehensive Features
extraction_settings = ComprehensiveFCParameters()
X = extract_features(df_shift, column_id="id", column_sort="time", column_value="value", impute_function=impute,show_warnings=False,default_fc_parameters=extraction_settings)

 

从上面的输出中,我们可以看到大约创建了800个特征。tsfresh还有助于基于p值的特征选择。更多详细信息可以查看Github: https://github.com/blue-yonder/tsfresh
官方文档 https://tsfresh.readthedocs.io/en/latest/index.html

2、autots

AutoTS 是一个自动化的时间序列预测库,可以使用简单的代码训练多个时间序列模型,此库的一些最佳功能包括:

让我们以苹果股票数据集为例,更详细地了解一下:

# Loading the package
from autots import AutoTS
import matplotlib.pyplot as plt
import pandas as pd

# Reading the data
df = pd.read_csv('../input/apple-aapl-historical-stock-data/HistoricalQuotes.csv')

# Doing some preprocessing
def remove_dollar(x):
    return x[2:]
df[' Close/Last'] = df[' Close/Last'].apply(remove_dollar)
df[' Close/Last']  = df[' Close/Last'].astype(float)
df['Date'] = pd.to_datetime(df['Date'])

# Plot to see the data:
df = df[["Date", " Close/Last"]]
df["Date"] = pd.to_datetime(df.Date)
temp_df = df.set_index('Date')
temp_df[" Close/Last"].plot(figsize=(12, 8), title="Apple Stock Prices", fontsize=20, label="Close Price")
plt.legend()
plt.grid()
plt.show()

model = AutoTS(forecast_length=40, frequency='infer', ensemble='simple', drop_data_older_than_periods=100)
model = model.fit(df, date_col='Date', value_col=' Close/Last', id_col=None)

 这将运行数百个模型。你将在输出窗格中看到运行的各种模型。让我们看看模型如何预测:

prediction = model.predict()
forecast = prediction.forecast
print("Stock Price Prediction of Apple")
print(forecast)

temp_df[' Close/Last'].plot(figsize=(15,8), title= 'AAPL Stock Price', fontsize=18, label='Train')
forecast[' Close/Last'].plot(figsize=(15,8), title= 'AAPL Stock Price', fontsize=18, label='Test')
plt.legend()
plt.grid()
plt.show()

 

更多详细信息可以查看 Github: https://github.com/winedarksea/AutoTS
官网文档: https://winedarksea.github.io/AutoTS/build/html/source/tutorial.html

3、Prophet

Prophet是Facebook研究团队开发的知名时间序列软件包,于2017年首次发布,适用于具有强烈季节性影响的数据和多个季节的历史数据。它具有高度的用户友好性和可定制性,只需进行最少的设置。

让我们看一个简单的例子:

# Loading the library
import pandas as pd
import matplotlib.pyplot as plt
from fbprophet import Prophet


# Loading the data from the repo:
df = pd.read_csv("https://raw.githubusercontent.com/facebook/prophet/master/examples/example_wp_log_peyton_manning.csv")

# Fitting the model
model = Prophet() 
model.fit(df) #fit the  model.

# Predict
future = model.make_future_dataframe(periods=730) # predicting for ~ 2 years
forecast = model.predict(future) # Predict future

# Plot results
fig1 = model.plot(forecast) # Plot the fit to past data and future forcast.
fig2 = model.plot_components(forecast) # Plot breakdown of components.
plt.show()
forecast # Displaying various results in table format.

趋势图和季节性图如下所示: 

我们还可以看到预测以及所有的置信区间

更多详细信息可以查看Github: https://github.com/facebook/prophet

文档: https://facebook.github.io/prophet/

4、darts:

Darts 是另一个 Python 包,它有助于时间序列的操作和预测。语法是“sklearn-friendly”,使用fit和predict函数来实现目标。此外,它还包含了从 ARIMA 到神经网络的各种模型。

该软件包最好的部分是它不仅支持单变量,而且还支持多变量时间序列和模型。该库还可以方便地对模型进行回溯测试,并将多个模型的预测和外部回归组合起来。让我们举一个简单的例子来了解它的工作原理:

#Loading the package
from darts import TimeSeries
from darts.models import ExponentialSmoothing
import matplotlib.pyplot as plt

# Reading the data
data = pd.read_csv('../input/air-passengers/AirPassengers.csv')
series = TimeSeries.from_dataframe(data, 'Month', '#Passengers')
print(series)

 

# Splitting the series in train and validation set
train, val = series.split_before(pd.Timestamp('19580101'))

# Applying a simple Exponential Smoothing model
model = ExponentialSmoothing()
model.fit(train)

# Getting and plotting the predictions
prediction = model.predict(len(val))series.plot(label='actual')
prediction.plot(label='forecast', lw=3)
plt.legend()

 

更多详细信息可以查看Github: https://github.com/unit8co/darts
文档: https://unit8co.github.io/darts/README.html 

5、AtsPy

AtsPy 代表Python 中的自动时间序列模型。该库的目标是预测单变量时间序列。你可以加载数据并指定要运行的模型,如下例所示:

# Importing packages
import pandas as pd
from atspy import AutomatedModel

# Reading the data:
data = pd.read_csv('../input/air-passengers/AirPassengers.csv')

# 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)

该软件包提供了一组完全自动化的不同模型。以下是可用型号的截图:

Github: https://github.com/firmai/atspy

6、kats:

Kats 是 Facebook 研究团队最近开发的另一个专门处理时间序列数据的库。该框架的目标是为解决时间序列问题提供一个完整的解决方案。使用此库,我们可以执行以下操作:

它刚刚发布了第一个版本。一些教程可以在这里找到
https://github.com/facebookresearch/Kats/tree/master/tutorials

7、sktime:

Sktime 库是一个统一的 python 库,它适用于时间序列数据,并且与 scikit-learn 兼容。它有时间序列预测、回归和分类模型,开发的主要目标是与 scikit-learn 进行互操作。

举个预测例子来介绍 sktime 的使用方法

from sktime.datasets import load_airline
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.theta import ThetaForecaster
from sktime.performance_metrics.forecasting import mean_absolute_percentage_error

y = load_airline()
y_train, y_test = temporal_train_test_split(y)
fh = ForecastingHorizon(y_test.index, is_relative=False)
forecaster = ThetaForecaster(sp=12)  # monthly seasonal periodicity
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
mean_absolute_percentage_error(y_test, y_pred)
>>> 0.08661467738190656

 

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标签:Python,工具包,df,pd,序列,import,model,data
来源: https://blog.csdn.net/m0_54214980/article/details/121707675