Overview:
This project applies advanced time series forecasting and machine learning models to predict future market trends across industries. From stock price movements to product demand cycles, the system identifies patterns using historical data and delivers actionable insights for strategic business planning.
Key Features:
- Trend Detection: Identifies upward/downward trends using moving averages, momentum indicators, and trendlines.
- Seasonality Analysis: Decomposes time series data to understand recurring patterns and peak performance periods.
- Predictive Modeling: Implements ARIMA, Prophet, and LSTM models for short-term and long-term trend forecasts.
- Anomaly Detection: Flags irregular spikes, drops, or unusual market behavior for proactive response.
- Backtesting & Validation: Uses cross-validation and performance metrics (MAE, RMSE, MAPE) to ensure model reliability.
Tools & Technologies:
- Python · Pandas · Prophet · ARIMA · LSTM · Seaborn · Matplotlib
- Jupyter Notebook · Scikit-learn · TensorFlow
Applications:
- Stock Market Prediction: Forecast stock trends and investment risks.
- E-commerce Demand Planning: Anticipate seasonal spikes or dips in product sales.
- Cryptocurrency Volatility Forecasting: Predict price shifts and optimize trading strategies.
- Retail Trend Analysis: Align inventory and marketing with future consumer behavior.
Visual Highlights (suggested visuals):
- Forecast plots showing predicted vs. actual trends
- Seasonality charts and heatmaps
- Model accuracy graphs with confidence intervals
- Market anomaly detection timeline
Benefits:
- Enables data-driven strategic planning
- Reduces risk in investment decisions
- Enhances operational efficiency with demand forecasting
Result:
Achieved over 87% accuracy in multi-step forecasting tasks, helping businesses react faster to market changes, optimize inventory, and capitalize on trend cycles.