def main(name):
transactions = timeseries.ReadData()
dailies = timeseries.GroupByQualityAndDay(transactions)
name = 'high'
daily = dailies[name]
PlotQuadraticModel(daily, name)
TestSerialCorr(daily)
PlotEwmaPredictions(daily, name)
# quadratic term
daily['years2'] = daily.years**2
model = smf.ols('ppg ~ years + years2', data=daily)
results = model.fit()
return model, results
#%%
# read data from timeseries.py
df = timeseries.ReadData()
df.head()
#%%
# group by quality
dailies = timeseries.GroupByQualityAndDay(df)
# select high for comparisons
name = 'high'
daily = dailies[name]
#%%
# run the quadratic model
model, results = RunQuadraticModel(daily)
results.summary()
#%%
# plot fitted values
timeseries.PlotFittedValues(model, results, label=name)
thinkplot.Config(title='Fitted Values',
xlabel='years',
def main(name):
transactions = timeseries.ReadData()
dailies = timeseries.GroupByQualityAndDay(transactions)
name = 'high'
daily = dailies[name]
PlotQuadraticModel(daily, name)