Build and evaluate deep learning models for financial time series in PyTorch, from recurrent networks to temporal convolutions and transformers, with rigorous validation.
Duration8 weeks
LevelAdvanced
Modules8
Lessons32
This course explores how neural network architectures can be applied to sequential financial data and where they struggle. You will implement recurrent, convolutional and attention-based models in PyTorch, design sequence datasets without leakage, and compare deep models against strong classical baselines. Emphasis is placed on regularization, uncertainty, reproducibility and honest model comparison on noisy, low signal-to-noise data.
8 modules · 32 lessons · 8 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Implement and compare LSTM and temporal convolutional models for realized volatility forecasting
Train a small transformer encoder on multivariate return sequences and analyze its attention patterns
Build a reproducible experiment harness that runs walk-forward training and reports results against baselines
Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.
7 weeks7 modulesApply supervised and unsupervised machine learning to financial datasets using feature engineering, purged cross-validation and disciplined, leakage-aware evaluation.
8 weeks8 modulesModel financial time series with stationarity tests, ARIMA, GARCH, cointegration and state-space methods, and evaluate forecasts with disciplined out-of-sample testing.
8 weeks8 modulesModel realized and implied volatility with GARCH, stochastic and local volatility approaches, build arbitrage-aware volatility surfaces, and run disciplined derivatives research.
Reserve your place in a few steps, or ask us anything about the curriculum, schedule and format first.
