import pandas as pd
import numpy as np
from matplotlib.pylab import rcParams
# Construct the data with gaps
ts = pd.read_csv('AirPassengers.csv', parse_dates=True, index_col='Month')
ts_ = ts.copy()
ts_.ix[67:79] = np.nan
ts_ = ts_.set_value('1961-12-01', '#Passengers', np.nan).asfreq('MS')
ssa = SSA(ts_)
# Plot original series for reference
ssa.view_time_series()
ssa.embed(embedding_dimension=36, suspected_frequency=12, verbose=True)
ssa.decompose(True)
ssa.view_s_contributions(adjust_scale=True)
# Component Signals
components = [i for i in range(13)]
rcParams['figure.figsize'] = 11, 2
for i in range(5):
ssa.view_reconstruction(ssa.Xs[i], names=i, symmetric_plots=i != 0)
rcParams['figure.figsize'] = 11, 4
# RECONSTRUCTION
ssa.view_reconstruction(*[ssa.Xs[i] for i in components], names=components)
# FORECASTING
ssa.forecast_recurrent(steps_ahead=48, plot=True)
