Zane Hassoun

I spent a few hours this week getting a better understanding of Long Short-Term Memory (LSTM) networks. They're an older neural net architecture by deep learning standards, but still useful, especially for sequential data like time series or text. I wanted to get a clearer sense of why they’re still in use despite the attention newer models (like transformers) get.

The basic idea is to extend the classic RNN by allowing the model to “remember” values over longer distances in the sequence. Regular RNNs suffer from vanishing gradients, which limits how far back they can effectively retain signal. LSTMs add gates (input, forget, output) to help manage this, selectively retaining or discarding values based on learned weights. It’s clever and not too hard to implement once you read through the equations.

I mostly focused on the time series side. I looked at a few examples of LSTMs being used for financial forecasting, not necessarily because I think they’re the best model for it, but because the framing is familiar. In one toy example, an LSTM was trained to predict stock returns based on price history. The results weren’t magical, but you can see how the structure makes sense: memory over prior days, conditional on sequence, not just raw features.

I’m not jumping to use LSTMs in anything right now, but it was a good reminder of how much mileage you can get out of relatively old architectures. They're still widely used in domains where data is inherently sequential and attention models are overkill or hard to train.

I’ll probably look at GRUs next, and eventually some of the hybrid models people use in forecasting competitions. Not sure if LSTMs will show up in anything I do soon, but they’re a good tool to have in the background.