Complex AI Forecasting Methods for Investments
summary
A complete AI investment platform is presented that combines ResNet/DenseNet, LSTM, GAN autoencoders for supervised forecasting with AlphaZero-style reinforcement learning for risk control and position sizing; the stack uses PyTorch/Lightning and Python libs (yfinance, Prophet, GluonTS/N-BEATS implementations) and shows a live demo and real deployment to a Luxembourg fund. Claims include >60% directional accuracy on some financial series, M4-inspired N-BEATS performance versus ARIMA/ETS baselines, and a PPO agent in OpenAI Gym that produces nontrivial profit in short backtests.
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chapters (9)
- 0:00AI forecasting for investments
- 2:00Advanced time series forecasting
- 4:07Machine learning breakthroughs
- 6:35AI time series methods
- 10:05Demo: N-BEATS for financial series
- 14:03Stock price prediction modeling
- 17:14Reinforcement learning strategies
- 21:06Evaluating RL agent
- 22:34Q&A
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