Financial Market Prediction: Time Series Forecasting

The Problem

Predicting financial market movements is one of the most challenging problems in data science. Traditional approaches often fail to capture the complex temporal patterns, volatility clustering, and seasonality inherent in financial time series data. Investors and analysts need reliable forecasting models that can account for these dynamics to make informed decisions.

My Approach

I built a comprehensive Python-based forecasting pipeline that implements and compares five advanced time series models:

ARIMA & SARIMA for capturing linear temporal dependencies and seasonal patterns in stock price movements. I performed extensive stationarity testing (ADF test, KPSS test) and used ACF/PACF plots to determine optimal lag orders.

ARCH & GARCH for modelling volatility clustering — the phenomenon where periods of high volatility tend to cluster together in financial markets. These models capture the heteroskedasticity that ARIMA-family models miss.

ARDL (Autoregressive Distributed Lag) for examining both short-run dynamics and long-run equilibrium relationships between variables.

Each model was rigorously evaluated using walk-forward validation rather than simple train/test splits, which better simulates real-world forecasting conditions. I compared models using RMSE, MAE, and directional accuracy metrics.

Key technical decisions:

• Used rolling window cross-validation to prevent look-ahead bias
• Implemented automatic parameter selection via grid search for ARIMA(p,d,q) orders
• Applied Box-Cox transformations to stabilise variance before modelling
• Built a modular pipeline allowing easy model swapping and comparison

Results

• Compared 5 models across multiple evaluation metrics and forecast horizons
• GARCH models significantly outperformed ARIMA for volatility forecasting, confirming the presence of volatility clustering in the dataset
• SARIMA captured seasonal patterns that improved forecast accuracy during predictable market cycles
• The walk-forward validation framework provides a reusable template for any time series forecasting task
• Full analysis documented with visualisations comparing predicted vs actual values across all models

What I Learned

Financial time series require multiple complementary models — no single approach captures all dynamics. The combination of trend-focused (ARIMA/SARIMA) and volatility-focused (ARCH/GARCH) models provides a more complete picture than any individual model alone.