def load_all_forecasts():
df = load_california_electricity_demand().sort_values("ds")
forecast_list = os.listdir(FORECAST_DIRECTORY)
for f in forecast_list:
df = df.merge(read_forecast(f), on="ds")
return df
import datetime
import numpy as np
import pandas as pd
from sts.models.prophet import (add_season_weekday_indicators,
seasonal_daily_prophet_model)
from sts.data.loader import load_california_electricity_demand
# Load all available data for training
df = load_california_electricity_demand()
# Take log transform for fully multiplicative model
df['y'] = df.y.apply(np.log)
# Fit best current model
model = seasonal_daily_prophet_model(df)
# Make predictions for one year ahead of most recent training data
future = add_season_weekday_indicators(
model.make_future_dataframe(periods=24 * 365, freq='H'))
forecast = model.predict(future)
samples = model.predictive_samples(future)
# Reverse log transform
predictions = np.exp(samples['yhat'])
# BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
# DATA.
#
# ###########################################################################
import os
import numpy as np
from sts.data.loader import load_california_electricity_demand
from sts.models.prophet import (add_season_weekday_indicators,
seasonal_daily_prophet_model)
# Load the training data (through 2018)
df = load_california_electricity_demand(train_only=True)
# Log transform the target variable
df['y'] = df.y.apply(np.log)
# ## Prophet (with more complicated seasonality)
# FB Prophet model, splitting intra-day seasonalities into four subgroups:
# - summer weekday
# - summer weekend
# - winter weekday
# - winter weekend
model = seasonal_daily_prophet_model(df)
future = model.make_future_dataframe(periods=8760, freq='H')
seasonal_future = add_season_weekday_indicators(future)
# identified above.
#
# This code is provided to you pursuant a written agreement with
# (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
# this code. If you do not have a written agreement with Cloudera nor
# with an authorized and properly licensed third party, you do not
# have any rights to access nor to use this code.
#
# Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
# contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
# KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
# WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
# IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
# AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
# ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
# OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
# CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
# RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
# BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
# DATA.
#
# ###########################################################################
from sts.data.loader import load_california_electricity_demand
# This will load or download the data as json, and write it to csv.
df = load_california_electricity_demand('data/demand.json')
df.to_csv('data/demand.csv')
