Import packages¶
In [1]:
import pandas as pd
import numpy as np
import plotly.plotly as ply
import cufflinks as cf
US Energy Production Data¶
Data Source: https://fred.stlouisfed.org/series/IPG2211A2N
In [2]:
ts = pd.read_csv('../../input/energy_production/IPG2211A2N.csv', index_col='DATE',
header=0,
names = ['DATE', 'value'],
parse_dates=['DATE'], )
In [3]:
start = pd.Timestamp('1985-01-01')
end = pd.Timestamp('2005-12-01')
ts = ts.loc[start:end, :]
In [4]:
ts.head()
Out[4]:
Plotting the raw time series¶
In [5]:
ts.iplot(title="Industrial Production 1985-2015",
xTitle='Year', yTitle='Production')
Out[5]:
Log Transformation of Time Series¶
In [6]:
np.log(ts).iplot(title="Log Transformation of Time Series",
xTitle='Year', yTitle='Production')
Out[6]:
Log Transformation + Detrend of Time Series¶
In [7]:
from scipy.signal import detrend
pd.Series(detrend(np.log(ts.value)), index=ts.index).iplot(title="Log Transformation + Detrend",
xTitle='Year', yTitle='Production', )
Out[7]:
Box-Cox Transformation¶
In [8]:
# from scipy.stats import boxcox
# pd.Series(detrend(boxcox((ts.value), lmbda=0.5, alpha=None)), index=ts.index).iplot(title="Industrial Production 1985-2018",
# xTitle='Year', yTitle='Production')
ACF Plot¶
In [9]:
from statsmodels.graphics.tsaplots import plot_acf
stat_ts = pd.Series(detrend(np.log(ts.value)), index=ts.index)
plot_acf(stat_ts)
Out[9]:
PACF Plot¶
In [10]:
from statsmodels.graphics.tsaplots import plot_pacf
plot_pacf(stat_ts)
Out[10]:
