Predict Next Sequence using Deep Learning in Python

In this tutorial, we’ll learn about the Prediction of the Next Sequence using Deep Learning in Python.

The next sequence prediction means predicting the next value of a given input sequence.

For example, if the input sequence contains the values [0, 0.1, 0.2, 0.3] then the next predicted sequence should be [0.4].
To better understand this topic we’ll work on a real-life example which is the prediction of Stock prices. For this, we’ll use LSTM concepts.

We’ll work on NIFTY50 data from 19/06/18 to 18/06/19 which is available on www.nseindia.com. It consists of “Date”, “Open”, “High”, “Low”, “Close”, “Shares Traded”, and “Turnover (Rs. Cr)”.

First import the following Python packages like Pandas, Numpy, Matplotlib, Keras, etc. as shown below:

import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import r2_score
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam
from keras.layers import LSTM
from keras.layers import Dense, Activation, Embedding, Dropout, TimeDistributed,Input

Now, we’ll read the data from the data file using pandas.

df = pd.read_csv('nse50_data.csv')
print(df[:5])

          Date      Open      High  ...     Close  Shares Traded  Turnover (Rs. Cr)
0  19-Jun-2018  10789.45  10789.45  ...  10710.45      231382790           12290.16
1  20-Jun-2018  10734.65  10781.80  ...  10772.05      199467082           10858.35
2  21-Jun-2018  10808.45  10809.60  ...  10741.10      230507383           12211.18
3  22-Jun-2018  10742.70  10837.00  ...  10821.85      236898415           13520.01
4  25-Jun-2018  10822.90  10831.05  ...  10762.45      236693278           12012.41

A graphical representation of the Turnovers (in crores) is shown below.

data = df.iloc[:,6].values
plt.figure(figsize=(10, 6))
plt.xlabel('Days')
plt.ylabel('Turnover (in crores)')
plt.plot(data)

We’ll use the turnover(in crores) data from 19/06/18 to 18/04/19 as train data and from 19/04/19 to 19/06/19 as test data.

df['Date'] = pd.to_datetime(df['Date'])  
mask = (df['Date'] == '2019-4-18')
print(df.loc[mask])     # index for the date 18-Apr-2019
print('--------------------------------------------')
train = data[:205]
test = data[175:]

          Date      Open      High  ...    Close  Shares Traded  Turnover (Rs. Cr)
205 2019-04-18  11856.15  11856.15  ...  11752.8      339653709           18271.27

[1 rows x 7 columns]
--------------------------------------------

Now, we’ll Normalize the train and test data using a min-max scaler.

sc = MinMaxScaler(feature_range = (0, 1))
train = sc.fit_transform(train.reshape(-1,1))
test = sc.transform(test.reshape(-1,1))

We’ll take timesteps = 30, i.e, take the first 30 days of data as input to predict the turnover on the 31st day. Create X_train using 30 timesteps for each sample.

X_train = []
y_train = []
for i in range(30, train.shape[0]):
    X_train.append(train[i-30:i, 0])
    y_train.append(train[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)

print(X_train.shape, y_train.shape)
print(X_train)
print(y_train[:2])

(175, 30) (175,)
[[0.32014897 0.27753191 0.31779817 ... 0.59711237 0.40685077 0.39237244]
 [0.27753191 0.31779817 0.35675479 ... 0.40685077 0.39237244 0.40965785]
 [0.31779817 0.35675479 0.31188189 ... 0.39237244 0.40965785 0.38402232]
 ...
 [0.49944087 0.76165063 0.40110533 ... 0.43010574 0.61685008 0.38092919]
 [0.76165063 0.40110533 0.48890961 ... 0.61685008 0.38092919 0.35909428]
 [0.40110533 0.48890961 0.48566231 ... 0.38092919 0.35909428 0.41972985]]
[0.40965785 0.38402232]

We’ll now design the model. We’ll use a single LSTM layer with 16 neurons and four dense layers having 8,4,2, and 1 neurons, respectively. We’ll use Adam optimizer and Mean-squared-error as a loss function.

# Training LSTM model

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
model = Sequential()
# LSTM layer
model.add(LSTM(16, input_shape=(X_train.shape[1], 1), activation='relu',kernel_initializer='lecun_uniform'))
#  Dense layer
model.add(Dense(8))
model.add(Dense(4))
model.add(Dense(2))
model.add(Dense(1))
model.compile(optimizer = 'adam', loss = 'mean_squared_error')

model.fit(X_train, y_train, epochs = 45, batch_size = 4)

Now, we’ll  Create X_test using 30 timesteps for each sample.

X_test = []
y_test = []

for i in range(30, test.shape[0]):
    X_test.append(test[i-30:i, 0])
    y_test.append(test[i, 0])
X_test, y_test = np.array(X_test), np.array(y_test)
print(X_test.shape)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
print(X_train.shape)

(40, 30)
(175, 30, 1)

Now, we’ll plot the predictions VS real turnover on the training set.

predicted = model.predict(X_train)
predicted = sc.inverse_transform(predicted)
plt.plot(sc.inverse_transform(train[-175:]), color = 'blue', label = 'Turnover')
plt.plot(predicted, color = 'yellow', label = 'Predicted Turnover')
plt.title('NIFTY50 Turnover')
plt.xlabel('Time')
plt.ylabel('Turnover')
plt.legend()
plt.show()

The result is as follows:

Now, we’ll plot the predictions VS real turnover on the test set.

predicted = model.predict(X_test)
predicted = sc.inverse_transform(predicted)
plt.plot(sc.inverse_transform(test[-41:]), color = 'blue', label = 'Turnover')
plt.plot(predicted, color = 'yellow', label = 'Predicted Turnover')
plt.title('NIFTY50 Turnover')
plt.xlabel('Time')
plt.ylabel('Turnover')
plt.legend()
plt.show()

The result is as follows:

I hope you enjoyed this tutorial.