LSTM
Long Short-Term Memory Network · Also known as: LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells
LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.
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When to use it
A good fit for forecasting and prediction on continuous or date-indexed sequences with a time-series or longitudinal structure, when long-range temporal patterns matter and a large dataset (about 500 observations or more) is available. Inputs must be normalised, and GPU acceleration is recommended. On smaller samples the network overfits, so simpler models such as Random Forest (below 500) or XGBoost (below 200) are safer.
Strengths & limitations
- Learns long-term dependencies in sequential data that ordinary recurrent networks lose.
- Well suited to time-series and longitudinal forecasting and sequence prediction.
- Assumption-light on distribution: does not require normally distributed data.
- Handles continuous and date-indexed sequence inputs natively.
- Needs a large dataset (around 500 observations or more) to train reliably.
- Overfits on small samples, where simpler machine-learning models generalise better.
- Inputs must be normalised before training, adding a mandatory preprocessing step.
- Computationally heavy; GPU acceleration is strongly preferred for practical training.
Frequently asked
How much data does an LSTM need?
It is intended for large datasets — roughly 500 observations or more. Below that the network tends to overfit and generalises poorly, so a simpler model such as Random Forest is the recommended fallback, and below about 200 observations XGBoost is a better choice.
Do I have to normalise the inputs?
Yes. Normalisation is a mandatory preprocessing step for LSTM; unscaled inputs destabilise training and degrade the result.
What makes LSTM different from a plain recurrent network?
LSTM adds a memory cell and gates that control what to remember, forget, and output. This lets it carry information across long sequences and learn long-term dependencies that a plain recurrent network loses to vanishing gradients.
Is a GPU required?
It is not strictly required, but GPU acceleration is strongly preferred because LSTM training on large sequential datasets is computationally heavy.
Sources
- Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735 ↗
How to cite this page
ScholarGate. (2026, June 1). Long Short-Term Memory Network. ScholarGate. https://scholargate.app/en/deep-learning/lstm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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