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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import pickle
import joblib
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error
import warnings
warnings.filterwarnings('ignore')
# Try to import models
try:
from statsmodels.tsa.arima.model import ARIMA
import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import LSTM, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
MODELS_AVAILABLE = True
except ImportError as e:
MODELS_AVAILABLE = False
st.error(f"Required libraries not installed. Please install statsmodels and tensorflow. Error: {e}")
st.set_page_config(
page_title="Stock Price Forecasting: ARIMA vs LSTM",
page_icon="📈",
layout="wide"
)
# Title and description
st.title("Stock Price Forecasting: ARIMA vs LSTM")
st.markdown("""
This application demonstrates stock price forecasting using traditional statistical methods (ARIMA)
and deep learning approaches (LSTM). Upload your stock data to get predictions and performance comparisons.
""")
# Sidebar for model selection and parameters
st.sidebar.header("Model Configuration")
model_choice = st.sidebar.selectbox(
"Select Forecasting Model",
["ARIMA", "LSTM", "Both Models"]
)
forecast_days = st.sidebar.slider("Forecast Days", 1, 60, 30)
# File upload
st.header(" Data Upload")
uploaded_file = st.file_uploader(
"Upload your stock data CSV file",
type=['csv'],
help="Expected format: columns should include 'date', 'close', 'open', 'high', 'low', 'volume', 'Name'"
)
if uploaded_file is not None:
try:
# Load data
df = pd.read_csv(uploaded_file)
st.success("Data loaded successfully!")
# Display basic info
st.subheader("Dataset Overview")
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Total Records", len(df))
with col2:
st.metric("Companies", df['Name'].nunique() if 'Name' in df.columns else 1)
with col3:
st.metric("Date Range", f"{len(df)} days")
# Show sample data
st.dataframe(df.head(), use_container_width=True)
# Company selection
if 'Name' in df.columns:
companies = df['Name'].unique()
selected_company = st.selectbox("Select Company", companies)
stock_data = df[df['Name'] == selected_company].copy()
else:
selected_company = "Stock"
stock_data = df.copy()
# Data preprocessing
stock_data['date'] = pd.to_datetime(stock_data['date'])
stock_data = stock_data.sort_values('date').set_index('date')
# Display stock price chart
st.subheader(f"📈 {selected_company} Stock Price Over Time")
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(stock_data.index, stock_data['close'], linewidth=2)
ax.set_title(f"{selected_company} Closing Price")
ax.set_xlabel("Date")
ax.set_ylabel("Price ($)")
ax.grid(True, alpha=0.3)
st.pyplot(fig)
# Model predictions section
st.header(" Forecasting Results")
if st.button("Generate Forecasts", type="primary"):
with st.spinner("Training models and generating forecasts..."):
# Prepare data
ts_data = stock_data['close'].dropna()
results = {}
if model_choice in ["ARIMA", "Both Models"] and MODELS_AVAILABLE:
try:
# ARIMA Model
st.info("Training ARIMA model...")
# Simple ARIMA parameters (for demo)
arima_model = ARIMA(ts_data, order=(1, 1, 1))
fitted_arima = arima_model.fit()
# Generate forecast
arima_forecast = fitted_arima.forecast(steps=forecast_days)
results['ARIMA'] = {
'forecast': arima_forecast,
'model': fitted_arima
}
except Exception as e:
st.error(f"ARIMA model error: {str(e)}")
if model_choice in ["LSTM", "Both Models"] and MODELS_AVAILABLE:
try:
# LSTM Model (Real Neural Network Implementation)
# Create a cache key for this dataset
cache_key = f"lstm_model_{selected_company}_{len(ts_data)}"
# Prepare LSTM data
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(ts_data.values.reshape(-1, 1))
sequence_length = min(60, len(scaled_data) // 4)
if len(scaled_data) <= sequence_length + 10:
st.warning("Insufficient data for LSTM training. Need at least 70 data points.")
# Fallback to simple trend method
last_values = ts_data.tail(10)
trend = np.polyfit(range(len(last_values)), last_values, 1)[0]
lstm_forecast = [ts_data.iloc[-1] + trend * i for i in range(1, forecast_days + 1)]
results['LSTM (Trend Fallback)'] = {
'forecast': np.array(lstm_forecast),
'scaler': None
}
elif cache_key not in st.session_state:
# Train new LSTM model
st.info("Training LSTM model (this may take a minute)...")
# Set seeds for reproducibility
np.random.seed(42)
tf.random.set_seed(42)
def create_sequences(data, seq_length):
X, y = [], []
for i in range(len(data) - seq_length):
X.append(data[i:(i + seq_length)])
y.append(data[i + seq_length])
return np.array(X), np.array(y)
# Create training sequences
X, y = create_sequences(scaled_data, sequence_length)
# Split data for training (use 80% for training)
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:]
# Build LSTM model
model = Sequential([
LSTM(50, return_sequences=True, input_shape=(sequence_length, 1)),
Dropout(0.2),
LSTM(50, return_sequences=False),
Dropout(0.2),
Dense(25),
Dense(1)
])
model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
# Training with early stopping
early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
# Train the model
with st.spinner("Training LSTM neural network..."):
history = model.fit(
X_train, y_train,
batch_size=32,
epochs=50,
validation_data=(X_test, y_test),
callbacks=[early_stopping],
verbose=0
)
# Calculate model performance on test set
test_predictions = model.predict(X_test, verbose=0)
test_predictions = scaler.inverse_transform(test_predictions)
y_test_actual = scaler.inverse_transform(y_test)
lstm_test_rmse = np.sqrt(mean_squared_error(y_test_actual, test_predictions))
st.success(f"LSTM Training Complete - Test RMSE: ${lstm_test_rmse:.2f}")
# Cache the trained model and related data
st.session_state[cache_key] = {
'model': model,
'scaler': scaler,
'test_rmse': lstm_test_rmse,
'sequence_length': sequence_length
}
# Generate forecast
st.info("Generating LSTM predictions...")
last_sequence = scaled_data[-sequence_length:].reshape(1, sequence_length, 1)
lstm_forecast_scaled = []
current_sequence = last_sequence.copy()
for _ in range(forecast_days):
next_pred = model.predict(current_sequence, verbose=0)[0, 0]
lstm_forecast_scaled.append(next_pred)
current_sequence = np.roll(current_sequence, -1, axis=1)
current_sequence[0, -1, 0] = next_pred
lstm_forecast_scaled = np.array(lstm_forecast_scaled).reshape(-1, 1)
lstm_forecast = scaler.inverse_transform(lstm_forecast_scaled).flatten()
results['LSTM'] = {
'forecast': lstm_forecast,
'model': model,
'scaler': scaler,
'test_rmse': lstm_test_rmse
}
else:
# Use cached model
st.info("Using cached LSTM model...")
cached_data = st.session_state[cache_key]
model = cached_data['model']
scaler = cached_data['scaler']
lstm_test_rmse = cached_data['test_rmse']
sequence_length = cached_data['sequence_length']
# Prepare data for cached model
scaled_data = scaler.transform(ts_data.values.reshape(-1, 1))
# Generate forecast with cached model
st.info("Generating LSTM predictions...")
last_sequence = scaled_data[-sequence_length:].reshape(1, sequence_length, 1)
lstm_forecast_scaled = []
current_sequence = last_sequence.copy()
for _ in range(forecast_days):
next_pred = model.predict(current_sequence, verbose=0)[0, 0]
lstm_forecast_scaled.append(next_pred)
current_sequence = np.roll(current_sequence, -1, axis=1)
current_sequence[0, -1, 0] = next_pred
lstm_forecast_scaled = np.array(lstm_forecast_scaled).reshape(-1, 1)
lstm_forecast = scaler.inverse_transform(lstm_forecast_scaled).flatten()
results['LSTM'] = {
'forecast': lstm_forecast,
'model': model,
'scaler': scaler,
'test_rmse': lstm_test_rmse
}
except Exception as e:
st.error(f"LSTM model error: {str(e)}") # Display results
if results:
# Create forecast dates
last_date = stock_data.index[-1]
forecast_dates = pd.date_range(
start=last_date + timedelta(days=1),
periods=forecast_days,
freq='B'
)
# Plot forecasts
st.subheader("Forecast Visualization")
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
# Historical + Forecast plot
historical_period = 180
hist_data = stock_data['close'].iloc[-historical_period:]
ax1.plot(hist_data.index, hist_data.values,
label='Historical', color='black', linewidth=2)
colors = ['blue', 'red']
styles = ['--', ':']
for i, (model_name, result) in enumerate(results.items()):
ax1.plot(forecast_dates, result['forecast'],
label=f'{model_name} Forecast',
color=colors[i], linestyle=styles[i], linewidth=2)
ax1.set_title(f"{selected_company} Stock Price Forecast")
ax1.set_xlabel("Date")
ax1.set_ylabel("Price ($)")
ax1.legend()
ax1.grid(True, alpha=0.3)
ax1.tick_params(axis='x', rotation=45)
# Forecast comparison
if len(results) > 1:
forecast_df = pd.DataFrame({
'Date': forecast_dates,
**{name: result['forecast'] for name, result in results.items()}
})
x = np.arange(min(10, len(forecast_dates)))
width = 0.35
models = list(results.keys())
for i, model in enumerate(models):
ax2.bar(x + i * width, results[model]['forecast'][:len(x)],
width, label=model, color=colors[i], alpha=0.7)
ax2.set_title('First 10 Days Forecast Comparison')
ax2.set_xlabel('Forecast Day')
ax2.set_ylabel('Predicted Price ($)')
ax2.set_xticks(x + width / 2)
ax2.set_xticklabels([f'Day {i+1}' for i in range(len(x))])
ax2.legend()
ax2.grid(True, alpha=0.3)
plt.tight_layout()
st.pyplot(fig)
# Summary table
st.subheader(" Forecast Summary")
current_price = stock_data['close'].iloc[-1]
summary_data = []
summary_data.append(['Current Price', f'${current_price:.2f}'])
for model_name, result in results.items():
# Handle different forecast types (pandas Series or numpy array)
if hasattr(result['forecast'], 'iloc'):
forecast_price = result['forecast'].iloc[-1]
else:
forecast_price = result['forecast'][-1]
price_change = ((forecast_price - current_price) / current_price) * 100
summary_data.append([
f'{model_name} Forecast ({forecast_days}d)',
f'${forecast_price:.2f}'
])
summary_data.append([
f'{model_name} Change (%)',
f'{price_change:+.1f}%'
])
summary_df = pd.DataFrame(summary_data, columns=['Metric', 'Value'])
st.dataframe(summary_df, use_container_width=True)
# Download forecast data
if len(results) > 0:
forecast_export = pd.DataFrame({
'Date': forecast_dates,
**{f'{name}_Forecast': result['forecast']
for name, result in results.items()}
})
csv = forecast_export.to_csv(index=False)
st.download_button(
label="📥 Download Forecast Data",
data=csv,
file_name=f"{selected_company}_forecast_{datetime.now().strftime('%Y%m%d')}.csv",
mime="text/csv"
)
except Exception as e:
st.error(f"Error processing data: {str(e)}")
st.info("Please ensure your CSV has the required columns: date, close, open, high, low, volume, Name")
else:
st.info(" Please upload a CSV file to get started")
# Show example data format
st.subheader(" Expected Data Format")
example_data = pd.DataFrame({
'date': ['2023-01-01', '2023-01-02', '2023-01-03'],
'open': [100.0, 101.0, 99.5],
'high': [102.0, 103.0, 101.0],
'low': [99.0, 100.0, 98.0],
'close': [101.0, 102.5, 100.0],
'volume': [1000000, 1200000, 900000],
'Name': ['AAPL', 'AAPL', 'AAPL']
})
st.dataframe(example_data, use_container_width=True)
# Footer
st.markdown("---")
st.markdown("""
**DataSynthis ML Job Task** - Stock Price Forecasting Application
Built with Streamlit • Models: ARIMA & LSTM • Deployed on Hugging Face Spaces
""") |