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)