Python for Time Series Data Analysis (Udemy) 5

引き続きUdemyでPython for Time Series Data Analysisの学習を進めている。クラスのシラバスでは、これまででPython/numpy/pandasの基本学習は完了したので、Time Series Dataの解析・予測手法へ移っていく。内容は大ボリュームで最終的には全てを学習するが、まずは簡単に導入でき予測結果を評価できる手法を整備するために、FacebookのProphetライブラリの学習を進める。

Prophetは基本的には、日、週、月や年周期での時系列データの予測に使用できるようだ。時間の変数dsをdatetimeクラスで設定することが重要。簡単に予測が得られる。

from fbprophet import Prophet
import matplotlib.pyplot as plt
from datetime import datetime
%matplotlib inline

df = pd.read_csv('./UDEMY_TSA_FINAL/Data/BeerWineLiquor.csv')
df.info
df.columns=['ds','y']
df.head()
df['ds'] = pd.to_datetime(df['ds'])
df.head()
df.info()
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=24,freq='MS')
future.tail()
df.tail()
len(df)
len(future)
forcast = m.predict(future)
forcast.head()
forcast.columns
forcast[['ds','yhat_lower','yhat_upper','yhat']].tail(12)
m.plot(forcast)
xmin = datetime(2014,1,1)
xmax = datetime(2021,1,1)
plt.xlim(xmin,xmax)
forcast.plot('ds','yhat')
m.plot_components(forcast)

季節性のトレンドも簡単に分離可能。

一定期間学習を行い、その先の将来予測を行う例。

import pandas as pd
from fbprophet import Prophet
from fbprophet.diagnostics import cross_validation,performance_metrics
from fbprophet.plot import plot_cross_validation_metric
from datetime import datetime
from statsmodels.tools.eval_measures import rmse
df = pd.read_csv('./UDEMY_TSA_FINAL/Data/Miles_Traveled.csv')
df.info()
df.columns = ['ds','y']
df['ds'] = pd.to_datetime(df['ds'])
df.head()
df.plot(x='ds',y='y')
len(df)
train = df.iloc[:576]
test = df.iloc[576:]
m = Prophet()
m.fit(train)
future = m.make_future_dataframe(periods=12,freq='MS')
forecast = m.predict(future)
forecast.tail
ax = forecast.plot(x='ds',y='yhat',label='Predicitons',legend=True,figsize=(12,8))
xmin=datetime(2018,1,1)
xmax=datetime(2019,1,1)
test.plot(x='ds',y='y',label='True Test Data',legend=True,ax=ax,xlim=(xmin,xmax))

predictions = forecast.iloc[-12:]['yhat']
predictions
rmse(predictions,test['y'])
test.mean()
initial = 5*365
initial = str(initial) + ' days'
initial
period = 5*365
period = str(period) + ' days'
horizon = 365
horizon = str(horizon) + ' days'
df_cv = cross_validation(m,initial=initial,period=period,horizon=horizon)
df_cv.head()
len(df_cv)
performance_metrics(df_cv)
plot_cross_validation_metric(df_cv,metric='rmse')

トレンド転換点を探す例。

import pandas as pd
from fbprophet import Prophet
from fbprophet.plot import add_changepoints_to_plot

df = pd.read_csv('./UDEMY_TSA_FINAL/Data/HospitalityEmployees.csv')
df.columns = ['ds','y']
df['ds'] = pd.to_datetime(df['ds'])
df.plot(x='ds',y='y',figsize=(12,10))
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=12,freq='MS')
forecast = m.predict(future)

fig = m.plot(forecast)
a = add_changepoints_to_plot(fig.gca(),m,forecast)

季節性のトレンドを分析し、将来予測を行う例。

import pandas as pd
from fbprophet import Prophet
df = pd.read_csv('./UDEMY_TSA_FINAL/Data/airline_passengers.csv')
df.head()
df.columns = ['ds','y']
df['ds'] = pd.to_datetime(df['ds'])
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(50,freq='MS')
forecast = m.predict(future)
fig = m.plot(forecast)
fig = m.plot_components(forecast)
from fbprophet.plot import add_changepoints_to_plot
fig = m.plot_components(forecast)

from fbprophet.plot import add_changepoints_to_plot
fig = m.plot(forecast)
a = add_changepoints_to_plot(fig.gca(),m,forecast)

m = Prophet(seasonality_mode='multiplicative')
m.fit(df)
future = m.make_future_dataframe(50,freq='MS')
forecast = m.predict(future)
fig = m.plot(forecast)

ひとまずこのFbprophetを用いて株価予測を行えば、Algorithm Trade Systemの枠組み開発を進められそうだ。