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Time series forecasting plays a crucial role in various fields, including finance, meteorology, and economics. In this article, we develop a Long Short-Term Memory (LSTM) neural network to predict stock prices, using historical data from Google (GOOG). The model is implemented using Python, TensorFlow, and Keras. This guide covers data preprocessing, model architecture, training, evaluation, and visualization of results, ensuring a structured and professional approach to time series forecasting with LSTMs.

Financial markets exhibit complex, nonlinear, and dynamic behaviors, making stock price prediction a challenging task. Traditional statistical models like ARIMA and exponential smoothing often struggle to capture long-term dependencies in time series data.

In contrast, recurrent neural networks (RNNs), particularly LSTM networks, are designed to handle sequential dependencies by retaining information over extended time periods. In this study, we employ an LSTM-based model to forecast stock prices and explore its effectiveness in predicting financial trends.

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1. Importing Libraries & Data

1.1 Install dependencies

Before proceeding, ensure you have installed the following Python libraries:

pip install numpy pandas matplotlib tensorflow scikit-learn yfinance

1.2 Load required libraries

import numpy as np  
import pandas as pd  
import matplotlib.pyplot as plt  
import tensorflow as tf  
from tensorflow.keras.models import Sequential  
from tensorflow.keras.layers import LSTM, Dense, Dropout  
from sklearn.preprocessing import MinMaxScaler  
import yfinance as yf

1.3 Download stock data

We retrieve Google (GOOG) stock prices from Yahoo Finance for the past five years. For this model, we focus on predicting closing prices since they reflect the final market consensus for the trading day.

df = yf.download('GOOG', start='2019-01-01', end='2024-01-01')  
df = df[['Close']]  # Focus on closing prices

2. Data Preprocessing

2.1 Normalize data for better training

scaler = MinMaxScaler(feature_range=(0,1))  
df_scaled = scaler.fit_transform(df)

2.2 Create time series sequences

We transform the dataset into a supervised learning problem using a sliding window approach:

• Input (X) → Closing prices for the last 60 days

• Output (y) → Closing price of the next day

window_size = 60  # Lookback period
X, y = [], []

for i in range(window_size, len(df_scaled)):  
    X.append(df_scaled[i-window_size:i, 0])  
    y.append(df_scaled[i, 0])  

X, y = np.array(X), np.array(y)  
X = np.reshape(X, (X.shape[0], X.shape[1], 1))

3. Building the LSTM Model

Our model consists of:
Three LSTM layers to capture long-term dependencies
Dropout layers to prevent overfitting
A Dense output layer for final price prediction

model = Sequential([

    LSTM(50, return_sequences=True, input_shape=(X.shape[1], 1)),  
    Dropout(0.2),  
    LSTM(50, return_sequences=True),  
    Dropout(0.2),  
    LSTM(50),  
    Dropout(0.2),  
    Dense(1)  
])  

model.compile(optimizer='adam', loss='mean_squared_error')

4. Training & Testing

4.1 Split data for training & validation

We allocate 80% of the data for training and the rest for testing.

train_size = int(len(X) * 0.8)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]

4.2 Train the model

history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

5. Predictions & Visualization

5.1 Generate predictions

predictions = model.predict(X_test)  
predictions = scaler.inverse_transform(predictions.reshape(-1, 1))  
y_test_real = scaler.inverse_transform(y_test.reshape(-1, 1))

5.2 Plot results

plt.figure(figsize=(14,5))  
plt.plot(y_test_real, label='Actual Prices', color='blue')  
plt.plot(predictions, label='Predicted Prices', color='red')  
plt.title("Stock Price Prediction with LSTM")  
plt.xlabel("Time")  
plt.ylabel("Stock Price")  
plt.legend()  
plt.show()

This guide demonstrates how to implement an LSTM-based time series forecasting model in Python. While effective, financial time series forecasting remains inherently complex and unpredictable due to external factors like economic policies, global events, and market sentiment.