EEMD.py

#[1]
!pip install EMD-signal
!pip install auto_ml
!pip install pyhht
from auto_ml import Predictor
#from auto_ml.utils import get_boston_dataset
from auto_ml.utils_models import load_ml_model

from PyEMD import EEMD 
eemd = EEMD(max_imfs=8)
#pip install PeakUtils
from sklearn.preprocessing import MinMaxScaler, RobustScaler 
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, explained_variance_score

import matplotlib.pyplot as plt
import os 
from scipy.integrate import odeint
#keras
from keras.models import *
from keras.layers import * 
from keras.optimizers import *
from keras.callbacks import *
from keras import backend as K

#[2]
def series_to_supervised(data, n_in, n_out, dropnan=True):

	n_vars = 1 if type(data) is list else data.shape[1]
	df = DataFrame(data)
	cols, names = list(), list()
	# input sequence (t-n, ... t-1)
	for i in range(n_in, 0, -1):
		cols.append(df.shift(i))
		names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
	# forecast sequence (t, t+1, ... t+n)
	for i in range(0, n_out):
		cols.append(df.shift(-i))
		if i == 0:
			names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
		else:
			names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
	# put it all together
	agg = concat(cols, axis=1)
	agg.columns = names
	# drop rows with NaN values
	if dropnan:
		agg.dropna(inplace=True)
	return agg
  
  #[3] 
  %matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib

matplotlib.style.use('ggplot')

#[4]
data= pd.read_csv("/content/BE_price.csv")
import numpy as np  
data = np.fromfile("BE_price.csv", sep = "\n")
data.shape

#[5]
import numpy as np
data = np.genfromtxt("BE_price.csv", delimiter = ","); data = np.asarray(data); data.shape

#[6]
plt.figure(figsize=(16,5))
plt.plot(data)
plt.title("BE_price Time Series")
plt.show()

#[7]
#Scale the Data
scaler = MinMaxScaler(feature_range = (-1,1)) 
scaled_data = scaler.fit_transform(data.reshape(-1,1))
scaled_data.shape
scaled_data = np.squeeze(scaled_data)
scaled_data.shape

#[8]
scaled_data = np.transpose(scaled_data)

#[9]
# before you do the EMD, cut out the out of sample part so that the EMDs are not constructed with those future values and information contained within them
in_sample = scaled_data[:-17472]; out_sample = scaled_data[-17472:]
print(in_sample.shape)
print(in_sample)

#[10]
in_sample = in_sample.reshape(-1,1)
out_sample = out_sample.reshape(-1,1)
print(in_sample)

#[11]
#eemd for in-sample
eemd = EEMD()
eemd.extrema_detection = "simple"
t = np.linspace(0, in_sample.shape[0], in_sample.shape[0])
t = t.reshape(-1,1)
print(t.shape, in_sample.shape)
t = np.squeeze(t)
in_sample = np.squeeze(in_sample)

#[12]
#eemd for in-sample
imfs1 = eemd(in_sample, t, 9)
print(imfs1.shape)

#[13]