Lessons from Financial Time Series Forecasting
· 2 min read
- Time Series
- Python
- Statistics
- Finance
- ARIMA
- GARCH
For my MSc research, I built a comprehensive time series forecasting system for financial market prediction. Here’s what the textbooks don’t tell you about working with real financial data.
Stationarity Is Everything
The first rule of time series forecasting: your data must be stationary (constant mean, variance, and autocovariance over time). Financial data almost never is. I spent more time transforming data into a stationary form than on any other step. The ADF test says “stationary” more often than you’d think — always cross-check with the KPSS test and visual inspection of rolling statistics.
ARIMA Is a Starting Point, Not a Solution
ARIMA models are excellent for learning the fundamentals, but they assume constant variance — which financial data violently violates. Stock returns exhibit volatility clustering: calm periods cluster together, and turbulent periods cluster together. This is where GARCH models become essential.
The practical insight: use ARIMA for the mean equation (predicting direction) and GARCH for the variance equation (predicting volatility). Together, they capture dynamics that neither can alone.
Walk-Forward Validation Is Non-Negotiable
Standard cross-validation (random splits) is invalid for time series because it leaks future information. I implemented walk-forward validation: train on data up to time T, forecast T+1, then expand the training window and repeat. This is slower but gives honest performance estimates.
The Metrics That Matter
RMSE and MAE tell you about magnitude error, but for financial forecasting, directional accuracy often matters more. Did the model correctly predict whether the price would go up or down? A model with higher RMSE but better directional accuracy can be more profitable than one with lower RMSE that frequently gets the direction wrong.
Key Takeaway
Financial time series forecasting humbles you. Models that look great in-sample often fall apart out-of-sample. The combination of rigorous statistical testing, multiple complementary models, and honest validation is what separates useful forecasts from curve-fitting.