) # use ANN with the default layer structure "a"
# the default layer structure "a" is (# of attributes + # of classes) / 2, here is layers= [2]
if (steps != 0):
prediction = np.array(
result['predicted'][:] +
np.array(ARIMA_ts[mon - monlow])
[-steps - len(result['predicted'][:]):-steps])
else:
prediction = np.array(
result['predicted'][:] +
np.array(ARIMA_ts[mon - monlow])
[-len(result['predicted'][:]):])
error = weka._NorRMSE(
joined[mon - monlow]['ElNino_real']
[-len(result['predicted']):], prediction)
t = nn['t0'][-len(result['predicted']) - 1:-1]
if (i == 1 and j == 0 and k == 0 and l == 0):
res.append(prediction)
actual.append(joined[mon - monlow]['ElNino_real']
[-len(result['predicted']):])
time.append(t)
avg_RMSE[mon - monlow] += error
if (mon == monhigh - 1):
res = np.array(res)
time = np.array(time)
actual = np.array(actual)
).astype(np.float)[:109]
ncep_timel6 = np.load(
'C:/Users/User/Documents/Thesis/Hybrid_prediction_model/ClimateLearn/plotML/leadtimedata/2006-2012new/time6_2006to2015_NCEP.npy'
).astype(np.float)[:109]
ncep_obs = np.load(
'C:/Users/User/Documents/Thesis/Hybrid_prediction_model/ClimateLearn/plotML/leadtimedata/2006-2012new/obs_2006to2015_NCEP.npy'
).astype(np.float)[:109]
ncep_obs_time = np.load(
'C:/Users/User/Documents/Thesis/Hybrid_prediction_model/ClimateLearn/plotML/leadtimedata/2006-2012new/timeobs_2006to2015_NCEP.npy'
).astype(np.float)[:109]
#timen_ncep = ncep_time[(ncep_time[i]>=2005) & (ncep_time[i]<=maxtime)]
#ncep_monl3n = ncep_monl3[(ncep_time[i]>=2005) & (ncep_time[i]<=maxtime)]
rmse_ncep_3monl = weka._NorRMSE(ncep_monl3[15:], ncep_obs[15:])
cor3 = 0.32
mintimecor3 = 2004 + cor3
maxtimecor3 = 2014 + cor3
mintime3 = 2004 - cor3
maxtime3 = 2014
for i in range(1):
bestresa = np.array(best_res3)[(time3 >= mintime3) & (time3 <= maxtime3)]
timen = time3[(time3 >= mintime3) & (time3 <= maxtime3)] + cor3
rmse_ncep_3monl = weka._NorRMSE(
ncep_monl3[(ncep_timel3 >= timen[0])
& (ncep_timel3 <= timen[timen.shape[0] - 1])],
ARIMA_ts[m] = np.append(
ARIMA_ts[m],
weka.forecasts(ARIMA_ninosq, steps, params, resids,
ARIMA_ts[m])[-1])
ARIMA_ninosq = np.append(ARIMA_ninosq, (dic['ElNino'][i]))
if (i >= 1):
resids = np.append(
resids, ARIMA_ninosq[-1] - np.array(ARIMA_ts)[-1])
i += 1
steps = steps - 1
#Calculate Normalized Root Mean Squared Error
if (steps != 0):
nrmse_ARIMA = weka._NorRMSE(
np.array(ARIMA_ts[m])[:-steps],
dicti['ElNino'][steps:])
else:
nrmse_ARIMA = weka._NorRMSE(np.array(ARIMA_ts[m]),
dicti['ElNino'])
if (steps != 0):
plt.plot(np.array(ARIMA_ts[m])[:-steps])
plt.plot(dicti['ElNino'][steps:])
else:
plt.plot(np.array(ARIMA_ts[m]))
plt.plot(dicti['ElNino'])
plt.title('NRMSE:' + str(nrmse_ARIMA))
plt.show()
#Define the residual, to be predicted with ANN
# the default layer structure "a" is (# of attributes + # of classes) / 2, here is layers= [2]
#Add ARIMA prediction and ANN prediction to get final prediction
if (steps != 0):
prediction = np.array(
result['predicted'][:] +
np.array(ARIMA_ts[mon - monlow])
[-steps - len(result['predicted'][:]):-steps])
else:
prediction = np.array(
result['predicted'][:] +
np.array(ARIMA_ts[mon - monlow])
[-len(result['predicted'][:]):])
error = weka._NorRMSE(
joined[mon - monlow]['ElNino_real']
[-len(result['predicted']):], prediction)
t = nn['t0'][-len(result['predicted']) - 1:-1]
if (ens10):
imax = np.where(RMSE == max(RMSE))[0][0]
if (RMSE[imax] > error
and (np.absolute(prediction) < 5.).all()):
RMSE[imax] = error
ensemble10[imax]['prediction'] = prediction
ensemble10[imax]['NRMSE'] = error
ensemble10[imax]['s'] = s
ensemble10[imax]['actual'] = joined[
mon - monlow]['ElNino_real'][
-len(result['predicted']):]
ensemble10[imax]['time'] = t