Deep Learning for Time-Series Forecasting
Linear models (like regression) assume that A + B = C. Markets are non-linear; the whole is often greater than the sum of its parts. Deep Learning allows us to model these complex interactions.
Recurrent Neural Networks (RNNs)
Standard neural networks have no memory. They treat every input as independent. RNNs have a "hidden state" that loops back, allowing them to remember previous inputs. This is crucial for time-series data.
However, simple RNNs suffer from the "vanishing gradient" problem—they forget long-term dependencies. (e.g., a regime shift that happened 3 months ago).
LSTM & GRU
Long Short-Term Memory (LSTM) networks solve this with gating mechanisms (Input Gate, Forget Gate, Output Gate). They explicitly learn what to remember and what to forget.
We use LSTMs to model multi-timeframe interactions. For example, specific price action on the 5-minute chart might only be bullish if the daily trend is also up. An LSTM can learn this hierarchical relationship.
The Transformer Revolution
While LSTMs process data sequentially (slow), Transformers use "Attention" mechanisms to look at the entire sequence at once. They ask: "For this specific moment in time, which other moments in history are most relevant?"
Multi-Head Attention
We use Transformer encoders to identify "similarity patterns." If the current market setup (volatility, volume, price action) looks 85% similar to the 2008 crash and 10% similar to the 2020 crash, the model weights its prediction accordingly.
This allows for context-aware forecasting that adapts to the "narrative" of the market.
Implementation Challenges
Deep Learning is data-hungry. On daily data, there aren't enough samples. We focus our DL efforts on high-frequency intraday data (tick or 1-minute bars), where we have millions of training examples to feed these massive models without overfitting.