Apple's New Smart Display Confirms What This Startup Knew All Along

Apple has entered the smart home race with its new Smart Display, firing a $158B signal that connected homes are the future.

When Apple moves in, it doesn’t just join the market — it transforms it.

One company has been quietly preparing for this moment.

Their smart shade technology already works across every major platform, perfectly positioned to capture the wave of new consumers Apple will bring.

While others scramble to catch up, this startup is already shifting production from China to its new facility in the Philippines — built for speed and ready to meet surging demand as Apple’s marketing machine drives mass adoption.

With 200% year-over-year growth and distribution in over 120 Best Buy locations, this company isn’t just ready for Apple’s push — they’re set to thrive from it.

Shares in this tech company are open at just $1.90.

Apple’s move is accelerating the entire sector. Don’t miss this window.

Act Fast- Apple’s Halo effect is coming

_{Past performance is not indicative of future results. Email may contain forward-looking statements. See US Offering for details. Informational purposes only.}

🚀 Your Investing Journey Just Got Better: Premium Subscriptions Are Here! 🚀

It’s been 4 months since we launched our premium subscription plans at GuruFinance Insights, and the results have been phenomenal! Now, we’re making it even better for you to take your investing game to the next level. Whether you’re just starting out or you’re a seasoned trader, our updated plans are designed to give you the tools, insights, and support you need to succeed.

Here’s what you’ll get as a premium member:

• Exclusive Trading Strategies: Unlock proven methods to maximize your returns.

• In-Depth Research Analysis: Stay ahead with insights from the latest market trends.

• Ad-Free Experience: Focus on what matters most—your investments.

• Monthly AMA Sessions: Get your questions answered by top industry experts.

• Coding Tutorials: Learn how to automate your trading strategies like a pro.

• Masterclasses & One-on-One Consultations: Elevate your skills with personalized guidance.

Our three tailored plans—Starter Investor, Pro Trader, and Elite Investor—are designed to fit your unique needs and goals. Whether you’re looking for foundational tools or advanced strategies, we’ve got you covered.

Don’t wait any longer to transform your investment strategy. The last 4 months have shown just how powerful these tools can be—now it’s your turn to experience the difference.

👉 Explore Premium Plans Now

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.

Pay No Interest Until Nearly 2027 AND Earn 5% Cash Back

Some credit cards can help you get out of debt faster with a 0% intro APR on balance transfers. Transfer your balance, pay it down interest-free, and save money. FinanceBuzz reviewed top cards and found the best options—one even offers 0% APR into 2027 + 5% cash back!

Learn How To Apply Now

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.