01. Executive Overview
Financial time series exhibit non-stationarity, high volatility, and complex non-linear dependencies. This project evaluates whether modern deep learning architectures (Long Short-Term Memory networks) outperform traditional econometric models (AutoRegressive Integrated Moving Average) on daily closing price trajectories.
02. Data Trajectory Visualizer
Under our evaluation framework, the solid line represents Actual Ground-Truth Market Prices, while the dashed purple line represents LSTM Model Forecast Output across a 30-day evaluation window.
03. Model Architecture & Pipeline
The statistical ARIMA model was fitted using Augmented Dickey-Fuller (ADF) stationarity checks and differencing $d=1$, followed by grid search for optimal $(p, d, q)$ parameters. The LSTM network utilized a 60-day sliding lookback window, multi-layer LSTM cells with 0.2 dropout, and Adam optimization targeting Root Mean Squared Error (RMSE).
LSTM achieved a lower RMSE than ARIMA across the evaluation horizon, effectively modeling non-linear regime shifts during multi-step lookahead predictions.
04. Interactive Deployment
The models are wrapped into an interactive Streamlit dashboard allowing live ticker inspection, lookback parameter tuning, and residual error analysis.