def forecast_gru(station: str, index: str, start: datetime, end: datetime):
"""
Apply LSTM Net Forecast.
"""
if not os.getenv("LOGS_HOME"):
log_home = forecast_params['LOGS_HOME']
else:
log_home = os.getenv("LOGS_HOME")
gru_logger = init_logger(log_path=os.path.join(
log_home, 'gru_{0}.log'.format(start.strftime("%Y%m%d%H"))))
session = orm.create_session()
preds = dict()
while start <= end:
this_start = start - timedelta(
days=forecast_params['gru_params']['last_days'])
this_end = start
# this_train = query_obs_4h(
# session=session, station_name=station, start=this_start, end=this_end
# )
this_train = query_qc_4h(session=session,
station_name=station,
start=this_start,
end=this_end)
this_predict_start = start - timedelta(
hours=(forecast_params['gru_params']['IN_STEPS'] - 1) *
forecast_params['FREQ'])
this_predict_end = start
# this_predict = query_obs_4h(
# session=session, station_name=station, start=this_predict_start, end=this_predict_end
# )
this_predict = query_qc_4h(session=session,
station_name=station,
start=this_predict_start,
end=this_predict_end)
print("This Train Shape: {0}".format(this_train.shape))
print("This Predict Shape: {0}".format(this_predict.shape))
gru_logger.info("Query [{0}] data before [{1}] done.".format(
station, start.strftime("%Y/%m/%d %H")))
this_forecast = deepl_model(this_train=this_train,
this_predict=this_predict,
kernel="gru")
gru_logger.info("Forecast [{0}] [{1}] [{2}] done.".format(
station, start.strftime("%Y/%m/%d %H"), index))
insert_gru(session,
data=this_forecast[[index]],
this_time=start,
station_name=station)
gru_logger.info("ADD NEW [{0}] [{1}] [{2}] forecast data done.".format(
station, start.strftime("%Y/%m/%d %H"), index))
start += timedelta(hours=forecast_params['FREQ'])
session.close()
gru_logger.info("[{0}] close session done.".format(station))
def qc_entrance(station: str, index: str, start: datetime, end: datetime):
session = orm.create_session()
qc_input = query_qc_input(session=session,
station_name=station,
start=start,
end=end)
print(qc_input)
qc_output = quality_control(qc_input)
print(qc_output)
insert_qc_output(session=session, data=qc_output, station_name=station)
session.close()
def forecast_fbprophet(station: str, index: str, start: datetime,
end: datetime):
"""
Apply FbPropHet Forecast.
"""
if not os.getenv("LOGS_HOME"):
log_home = forecast_params['LOGS_HOME']
else:
log_home = os.getenv("LOGS_HOME")
fbprophet_logger = init_logger(log_path=os.path.join(
log_home, 'fbprophet_{0}.log'.format(start.strftime("%Y%m%d%H"))))
session = orm.create_session()
preds = dict()
while start <= end:
this_start = start - timedelta(
days=forecast_params['fbprophet_params']['last_days'])
this_end = start
#this_resp = query_obs_4h(session=session, station_name=station, start=this_start, end=this_end)
this_resp = query_qc_4h(session=session,
station_name=station,
start=this_start,
end=this_end)
this_data = this_resp[['time', index]].rename(columns={
'time': 'ds',
index: 'y'
})
fbprophet_logger.info(
"Query [{0}] history data before [{1}] done.".format(
station, start.strftime("%Y/%m/%d %H")))
this_forecast = fbprophet_model(this_data, start, index)
fbprophet_logger.info("Forecast [{0}] [{1}] [{2}]done.".format(
station, start.strftime("%Y/%m/%d %H"), index))
insert_fbprophet(session,
data=this_forecast,
this_time=start,
station_name=station,
index=index)
fbprophet_logger.info(
"ADD NEW [{0}] [{1}] [{2}] forecast data done.".format(
station, start.strftime("%Y/%m/%d %H"), index))
start += timedelta(hours=forecast_params['FREQ'])
session.close()
fbprophet_logger.info("[{0}] close session done.".format(station))
def forecast_arima(station: str, index: str, start: datetime, end: datetime):
"""
Apply ARIMA Forecast.
"""
if not os.getenv("LOGS_HOME"):
log_home = forecast_params['LOGS_HOME']
else:
log_home = os.getenv("LOGS_HOME")
arima_logger = init_logger(log_path=os.path.join(
log_home, 'arima_{0}.log'.format(start.strftime("%Y%m%d%H"))))
session = orm.create_session()
preds = dict()
while start <= end:
this_start = start - timedelta(
days=forecast_params['arima_params']['last_days'])
this_end = start
#this_resp = query_obs_4h(session=session, station_name=station, start=this_start, end=this_end)
this_resp = query_qc_4h(session=session,
station_name=station,
start=this_start,
end=this_end)
this_data = this_resp[['time', index]].set_index('time')
arima_logger.info("Query [{0}] history data before [{1}] done.".format(
station, start.strftime("%Y/%m/%d %H")))
this_forecast = arima_model(this_data)
print("*** {0} Forecast ***\n{1}".format(
start.strftime("%Y-%m-%d %H:00:00"), this_forecast))
arima_logger.info("Forecast [{0}] [{1}] [{2}] done.".format(
station, start.strftime("%Y/%m/%d %H"), index))
insert_arima(session,
data=this_forecast,
this_time=start,
station_name=station,
index=index)
arima_logger.info(
"ADD NEW [{0}] [{1}] [{2}] forecast data done.".format(
station, start.strftime("%Y/%m/%d %H"), index))
start += timedelta(hours=forecast_params['FREQ'])
session.close()
arima_logger.info("[{0}] close session done.".format(station))