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Dave Moore davmre

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View electricity_demand_model.py
temperature_effect = tfp.sts.LinearRegression(
design_matrix=tf.reshape(temperature - np.mean(temperature),
(-1, 1)), name='temperature_effect')
hour_of_day_effect = tfp.sts.Seasonal(
num_seasons=24,
observed_time_series=demand,
name='hour_of_day_effect')
day_of_week_effect = tfp.sts.Seasonal(
num_seasons=7,
num_steps_per_season=24,
@davmre
davmre / co2_model.py
Created Mar 4, 2019
Structural Time Series model of monthly atmospheric CO2 data from Mauna Loa
View co2_model.py
import tensorflow_probability as tfp
trend = tfp.sts.LocalLinearTrend(observed_time_series=co2_by_month)
seasonal = tfp.sts.Seasonal(
num_seasons=12, observed_time_series=co2_by_month)
model = tfp.sts.Sum([trend, seasonal], observed_time_series=co2_by_month)
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