Multi tool use

LSTM neural network for multiple steps time series prediction

I tried to develop a model that foresees two time-steps forward

In this regard I modified a GitHub code for the single step forecast coding a data_load function that takes n steps backward in the X_train/test series and set it against a y_train/test 2-array.

data_load

n steps

X_train/test

y_train/test

I set the neurons list to output in Dense a 2-vector object.

And last I wrote a predict function and a plot function for the 2-step-forecast.

I do not normalized features lables and forecasts I will do in the future.

After a bit of hyperfine tuning it returns a good score for the mse and rmse:

Train Score: 0.00000 MSE (0.00 RMSE)
Test Score: 0.00153 MSE (0.04 RMSE)


It can find quite well the trend, but it returns all forecasts with negative directions.

Does anyone have a suggestion?

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt2
import pandas as pd
from pandas import datetime
import math, time
import itertools
from sklearn import preprocessing
import datetime
from sklearn.metrics import mean_squared_error
from math import sqrt
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.recurrent import LSTM
from keras.models import load_model
import keras
from numpy import newaxis
pd.core.common.is_list_like = pd.api.types.is_list_like
import pandas_datareader.data as web
import h5py
from keras import backend as K
import quandl

quandl.ApiConfig.api_key = 'myQuandlKey'
seq_len = 2
shape = [seq_len, 9, 2]
neurons = [256, 256, 64, 2]
dropout = 0.2
decay = 0.5
epochs = 100
stock_name = 'AAPL'
global_start_time = time.time()

def get_stock_data(stock_name, normalize=True, ma=):
"""
Return a dataframe of that stock and normalize all the values.
(Optional: create moving average)
"""
df = quandl.get_table('WIKI/PRICES', ticker = stock_name)
df.drop(['ticker', 'open', 'high', 'low', 'close', 'ex-dividend', 'volume', 'split_ratio'], 1, inplace=True)
df.set_index('date', inplace=True)

# Renaming all the columns so that we can use the old version code
df.rename(columns={'adj_open': 'Open', 'adj_high': 'High', 'adj_low': 'Low', 'adj_volume': 'Volume', 'adj_close': 'Adj Close'}, inplace=True)

# Percentage change
df['Pct'] = df['Adj Close'].pct_change()
df.dropna(inplace=True)

# Moving Average
if ma != :
for moving in ma:
df['{}ma'.format(moving)] = df['Adj Close'].rolling(window=moving).mean()
df.dropna(inplace=True)

if normalize:
min_max_scaler = preprocessing.MinMaxScaler()
df['Open'] = min_max_scaler.fit_transform(df.Open.values.reshape(-1,1))
df['High'] = min_max_scaler.fit_transform(df.High.values.reshape(-1,1))
df['Low'] = min_max_scaler.fit_transform(df.Low.values.reshape(-1,1))
df['Volume'] = min_max_scaler.fit_transform(df.Volume.values.reshape(-1,1))
df['Adj Close'] = min_max_scaler.fit_transform(df['Adj Close'].values.reshape(-1,1))
df['Pct'] = min_max_scaler.fit_transform(df['Pct'].values.reshape(-1,1))
if ma != :
for moving in ma:
df['{}ma'.format(moving)] = min_max_scaler.fit_transform(df['{}ma'.format(moving)].values.reshape(-1,1))

# Move Adj Close to the rightmost for the ease of training
adj_close = df['Adj Close']
df.drop(labels=['Adj Close'], axis=1, inplace=True)
df = pd.concat([df, adj_close], axis=1)

#df.to_csv('aap.csv')
return df

df = get_stock_data(stock_name, ma=[50, 100, 200])

def plot_stock(df):
print(df.head())
plt.subplot(211)
plt.plot(df['Adj Close'], color='red', label='Adj Close')
plt.legend(loc='best')
plt.subplot(212)
plt.plot(df['Pct'], color='blue', label='Percentage change')
plt.legend(loc='best')
plt.show()

#plot_stock(df)

def load_data(stock, seq_len):
amount_of_features = len(stock.columns)
data = stock.values
sequence_length = seq_len + 2 # index starting from 0
result =

for index in range(len(data) - sequence_length): # maxmimum date = lastest date - sequence length
result.append(data[index: index + sequence_length]) # index : index + 22days

result = np.array(result)
row = round(0.8 * result.shape[0]) # 80% split

train = result[:int(row), :,:] # 80% date
X_train = train[:, :-2,:] # all data until day m
y_train = train[:, -2:,:][:,:,-1] # day m + 1 adjusted close price

X_test = result[int(row):, :-2,:]
y_test = result[int(row):, -2:,:][:,:,-1]

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], amount_of_features))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], amount_of_features))

print ("________________________________________________________________")
print ("X_train shape = {}".format(X_train.shape))
print ("y_train shape = {}".format(y_train.shape))
print ("")
print ("X_test shape = {}".format(X_test.shape))
print ("y_test shape = {}".format(y_test.shape))
print ("________________________________________________________________")

return [X_train, y_train, X_test, y_test]

X_train, y_train, X_test, y_test = load_data(df, seq_len)

def build_model(shape, neurons, dropout, decay):
model = Sequential()

model.add(LSTM(neurons[0], input_shape=(shape[0], shape[1]), return_sequences=True))
model.add(Dropout(dropout))

model.add(LSTM(neurons[1], input_shape=(shape[0], shape[1]), return_sequences=False))
model.add(Dropout(dropout))

model.add(Dense(neurons[2],kernel_initializer="uniform",activation='relu'))
model.add(Dense(neurons[3],kernel_initializer="uniform",activation='linear'))
# model = load_model('my_LSTM_stock_model1000.h5')
adam = keras.optimizers.Adam(decay=decay)
model.compile(loss='mse',optimizer='adam', metrics=['accuracy'])
model.summary()
return model

model = build_model(shape, neurons, dropout, decay)

model.fit(
X_train,
y_train,
batch_size=512,
epochs=epochs,
validation_split=0.01,
verbose=1)

def model_score(model, X_train, y_train, X_test, y_test):
trainScore = model.evaluate(X_train, y_train, verbose=0)
print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))

testScore = model.evaluate(X_test, y_test, verbose=0)
print('Test Score: %.5f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))
return trainScore[0], testScore[0]

model_score(model, X_train, y_train, X_test, y_test)

def percentage_difference(model, X_test, y_test):
percentage_diff=

p = model.predict(X_test)
for u in range(len(y_test)): # for each data index in test data
pr = p[u][0] # pr = prediction on day u

percentage_diff.append((pr-y_test[u]/pr)*100)
print('Prediction duration: ', str(datetime.timedelta(seconds=(time.time() - global_start_time))) )
return p

def denormalize(stock_name, normalized_value):
"""
Return a dataframe of that stock and normalize all the values.
(Optional: create moving average)
"""
df = quandl.get_table('WIKI/PRICES', ticker = stock_name)
df.drop(['ticker', 'open', 'high', 'low', 'close', 'ex-dividend', 'volume', 'split_ratio'], 1, inplace=True)
df.set_index('date', inplace=True)

# Renaming all the columns so that we can use the old version code
df.rename(columns={'adj_open': 'Open', 'adj_high': 'High', 'adj_low': 'Low', 'adj_volume': 'Volume', 'adj_close': 'Adj Close'}, inplace=True)

df.dropna(inplace=True)
df = df['Adj Close'].values.reshape(-1,1)
normalized_value = normalized_value.reshape(-1,1)

#return df.shape, p.shape
min_max_scaler = preprocessing.MinMaxScaler()
a = min_max_scaler.fit_transform(df)
new = min_max_scaler.inverse_transform(normalized_value)

return new

def plot_result(stock_name, normalized_value_p, normalized_value_y_test):
newp = denormalize(stock_name, normalized_value_p)
newy_test = denormalize(stock_name, normalized_value_y_test)
#newy_test = np.roll(newy_test,1,0)
plt2.plot(newp, color='red', label='Prediction')
plt2.plot(newy_test,color='blue', label='Actual')
plt2.legend(loc='best')
plt2.title('Global run time {}'.format(str(datetime.timedelta(seconds=(time.time() - global_start_time))) ) )
plt2.xlabel('Days')
plt2.ylabel('Adjusted Close')
plt2.show()

def predict_sequences_multiple(model, data, window_size, prediction_len):
#Predict sequence of 50 steps before shifting prediction run forward by 50 steps
prediction_seqs =
for i in range(int(len(data)/prediction_len)):
curr_frame = data[i*prediction_len]
predicted =
for j in range(prediction_len):
predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0)
prediction_seqs.append(predicted)

print('Prediction duration: ', str(datetime.timedelta(seconds=(time.time() - global_start_time))) )
return prediction_seqs

def plot_results_multiple(predicted_data, true_data, prediction_len):
fig = plt.figure(facecolor='white')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
#Pad the list of predictions to shift it in the graph to it's correct start
for i, data in enumerate(predicted_data):
padding = [None for p in range(i * prediction_len)]
plt.plot(padding + data, label='Prediction')
plt.legend()
plt.title('Global run time {}'.format(str(datetime.timedelta(seconds=(time.time() - global_start_time))) ) )
plt.show()

#Single step prediction
#p = percentage_difference(model, X_test, y_test)
#plot_result(stock_name, p, y_test)

#Multiple step prediction
predictions = predict_sequences_multiple(model, X_test, seq_len, 2)
plot_results_multiple(predictions, y_test, 2)


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