Introduction to Long Short-Term Memory (LSTM)

TL;DR: Long Short-Term Memory (LSTM) is designed for sequence data such as text, audio, and time series, using gates and a memory cell to maintain context and learn long-range patterns. This article explains what LSTM is and why it is used in NLP, forecasting, and other time-based problems.

Introduction

When working with sequences such as sentences, audio, or time-based data, remembering prior information is essential. Traditional neural networks often lose this context as new data comes in. Long Short-Term Memory was created to solve this problem by deciding which information to keep, which to update, and which to discard as the sequence progresses. So let’s start with the basics: what is LSTM?

In simple terms, an LSTM:

• Keeps useful past information
• Filters out what is no longer relevant
• Updates memory with new inputs
• Passes only meaningful context forward

In this article, you’ll learn what is long short-term memory, and how it works in sequence-based models. You’ll also get a clear view of its internal mechanism, practical usage, and how it compares with GRU and Transformer models.

What is LSTM and What it Does?

LSTM, short for Long Short-Term Memory, is a type of recurrent neural network, used in deep learning, built to learn from sequence data by keeping useful information for longer than a standard RNN can. It was introduced to address a key training problem in classic RNNs: the vanishing gradient problem. In standard RNNs, gradients shrink as they move backward through many time steps, so the model struggles to learn relationships that span long sequences.

LSTM solves this by adding a memory cell and gates that control what to store, what to forget, and what to pass forward. This structure helps important signals remain available over long intervals, which is why LSTMs work well for tasks like long text, speech, and time series, where early inputs can affect later predictions.

At this point, the answer to “what is LSTM” should already feel less abstract.

Applications of LSTM

RNN vs LSTM

Apart from understanding what is LSTM, it is also important to understand how its internal design differs from that of a basic Recurrent Neural Network. So, if you are comparing the two, what is LSTM doing that an RNN doesn’t? While both models process sequential data step by step, they store and update information in different ways, which affects their behavior during training and inference.

A traditional RNN maintains a single hidden state that gets replaced at every step, so past information must compete with new inputs. LSTM introduces an additional memory pathway that exists separately from the hidden state. This separation allows information to be carried forward more selectively, rather than being overwritten at each step.

Here is a fast way to frame what is LSTM is versus a basic RNN:

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LSTM Architecture: Cell State, Hidden State, and Gates

To go beyond definitions, what is LSTM made of? You have seen how LSTM compares with a traditional RNN. Now let's look at what makes the LSTM algorithm so effective at handling sequences:

1. Cell State

As seen in the diagram, the cell state is the thick horizontal path running through the LSTM cell. It carries memory forward from the previous step c(t-1) to the updated memory c(t). Instead of being rewritten each time, this memory is updated using two controlled operations: one part of the old memory is kept (after a filter), and one part of new information is added. This steady, mostly straight path is what helps LSTMs keep useful context over longer sequences and mitigate vanishing gradients compared with a basic RNN.

2. Hidden State

The hidden state is the output of the cell at each step, shown on the right as h(t). You can think of it as the “current snapshot” the model exposes to the next time step and to the output layer. In the diagram, h(t) is computed from the updated cell state c(t), filtered by the output gate, so it reflects both what the model remembers and what it chooses to reveal right now.

3. Gating Mechanisms

The gates are the control system in the diagram. They take the current input x(t) and the previous hidden state h(t-1), then produce three gate values that decide how memory is updated and what gets output. Visually, this is why you see filter outputs like f(t), i(t), and the output gate feeding into the multiply (×) and add (+) nodes along the cell state path. Let’s break down the main gates:

• Forget Gate

The forget gate f(t) decides how much of the previous cell state c(t-1) should be kept. In the diagram, f(t) flows into a multiply (×) node with c(t-1). If f(t) is close to 0, that part of old memory is mostly removed. If it is close to 1, it is mostly retained.

• Input Gate and Candidate

The input gate i(t) decides how much new information should be written into memory. The diagram shows i(t) working together with a candidate value (often written as c~). The candidate represents the new content the model could add, and the input gate scales how much of it gets written. That scaled write then joins the memory path at the add (+) node to form the updated cell state c(t).

• Output Gate

The output gate o(t) decides what part of the updated memory c(t) becomes the hidden state h(t). In the diagram, c(t) first passes through tanh, then the result is multiplied (×) by the output gate to produce h(t). This means the model can keep information in memory without always exposing it at every step.

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How to Use LSTM (Inputs, Shapes, Key Parameters)

Using an LSTM model correctly involves understanding the input shapes, choosing the right parameters, and avoiding common mistakes. Once you know what is LSTM, inputs and shapes get much easier. Let’s break these down step by step:

1. Preparing Input Data

The input data must be organized so the network can process sequences correctly. Each input should represent:

• Batch size: Number of sequences processed together at once
• Sequence length: Number of steps in each sequence, such as words in a sentence or time points in a time series
• Features per step: Values at each step, like word embeddings or sensor readings

For text data, words are first converted into embeddings so that semantic meaning. Sequences of different lengths are padded, and masking ensures that the model ignores these padded values during training. 

Structuring inputs this way ensures the LSTM model receives the data in the correct format and performs efficiently.

2. Key LSTM Parameters

Once your data is ready, the next consideration is configuring the LSTM layer to suit your task. A few parameters play a major role in model behavior:

• units: Specifies the quantity of memory cells in the LSTM layer. A larger number of units can capture more complicated patterns, but on the other hand, it will also pose a higher risk of overfitting and increased computational cost
• return_sequences: Affects the layer's output. When set to True, a sequence is returned at every timestep, which is crucial for stacking LSTM layers or for sequence-to-sequence tasks. In contrast, setting to False produces only the final output, which is appropriate for classification or regression based on the entire sequence
• Dropout: The dropout is applied to inputs, while the recurrent_dropout is to recurrent connections. They help to reduce overfitting without compromising the stability of the model
• Bidirectional LSTMs: Allow the model to capture the sequences from both ends, thus making the context in tasks like language processing clearer
• Stacked LSTMs: Adding multiple layers increases not only the model's depth but also its ability to represent different aspects; however, it requires careful tuning to ensure stability

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3. Common Pitfalls to Avoid

Even with correct shapes and parameters, there are traps that many practitioners fall into, especially with sequence data:

• Time leakage in splits: In the case of time-series or sequential prediction, the most appropriate way would be to apply forward chaining or rolling splits instead of random sampling. Random splits can leak future information into the training data, giving overly optimistic results
• Padding issues: If variable‑length sequences are padded, ensure masking is applied so the LSTM ignores the padded zeros. Otherwise, the model treats padding as real data points
• Confusing timesteps with features: Keep in mind that the length of the sequence (for example, words or time steps) is represented by timesteps, whereas dimensions per timestep (for instance, embedding size) are represented by features. Interchanging these will lead to shape mismatches
• Overfitting with large units: An LSTM with too many units and not enough regularization (dropout, recurrent_dropout) can memorize training sequences without learning general patterns

Did You Know? LSTM was introduced in 1997 to help RNNs learn long-range dependencies by keeping gradients from fading over many time steps. (Source: MIT)

Hands-on Example

To understand how an LSTM works, let’s try a simple sequence prediction task. We will train the LSTM to look at a few numbers in a sequence and predict the next one. This example will show how the model uses past inputs, updates its memory, and produces outputs.

Now, let’s break this down step by step:

Step 1: Import Libraries

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

Step 2: Prepare the Data

data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
X = []
y = []
timesteps = 3
for i in range(len(data) - timesteps):
 X.append(data[i:i+timesteps])
 y.append(data[i+timesteps])
X = np.array(X).reshape((X.shape[0], X.shape[1], 1))
y = np.array(y)

Step 3: Build the LSTM Model

model = Sequential()
model.add(LSTM(units=50, input_shape=(timesteps, 1)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

Step 4: Train the Model

model.fit(X, y, epochs=200, verbose=0)

Step 5: Make Predictions

test_input = np.array([7, 8, 9]).reshape((1, timesteps, 1))
predicted = model.predict(test_input, verbose=0)
print("Predicted next value:", predicted[0][0])

Because the dataset is tiny, the prediction may not be exactly 10 but something close (like 9.8 or 10.1).

LSTM vs GRU vs Transformer

This comparison helps clarify what is LSTM relative to newer approaches. While LSTM handles sequences effectively, other models like GRU and Transformer are also widely used for sequential data. They differ in design, complexity, speed, and performance, so let’s see how they compare with LSTM:

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Key Takeaways

• LSTM is designed to handle sequential data by preserving important past information, making it effective for text, audio, and time-series tasks where long-term context matters.
• Its memory cell and gating mechanism solve the vanishing gradient problem, allowing stable learning across long sequences where traditional RNNs fail.
• The correct utilization of LSTM hinges on accurately structured inputs, carefully made parameter choices, and avoiding common problems such as padding mistakes and data leakage.
• While LSTM works well for many sequence problems, GRU offers a lighter alternative, and Transformers excel when large data and long-range context are required.

FAQs

1. What is the difference between RNN and LSTM?

A basic RNN struggles to retain information over long sequences, while an LSTM uses a memory cell and gates to preserve important context for much longer. This makes LSTM more stable and reliable than a traditional RNN.

2. What is LSTM in Python?

In Python, LSTM is primarily used within deep learning frameworks such as TensorFlow or PyTorch to build sequence-based models for prediction, classification, or forecasting.

3. Is LSTM an AI model?

Yes, LSTM is an AI model used within neural networks to learn patterns from sequential data.

4. What is LSTM vs CNN?

LSTM caters to temporal and sequential data, and CNN is dedicated to spatial patterns, e.g., images or feature grids. Both are for different kinds of learning problems.

5. What is LSTM in deep learning?

In LSTM deep learning, LSTM is a specialized recurrent layer that enables models to learn long-term relationships within sequences.

6. What does LSTM stand for in machine learning?

LSTM stands for Long Short-Term Memory in machine learning.

7. How does a Long Short-Term Memory network work?

It processes data step by step while using gates to control what information is stored, updated, or passed forward in memory.

8. Why is LSTM better than a traditional RNN?

LSTM avoids the vanishing gradient problem, allowing it to learn long-range dependencies that traditional RNNs usually miss.

9. What problem does LSTM solve in neural networks?

LSTM solves the problem of losing crucial past information during training on long sequences.

10. What are the main components of an LSTM cell?

An LSTM cell consists of a cell state, hidden state, forget gate, input gate, and output gate.

11. How does LSTM handle long-term dependencies?

It uses a dedicated memory path that carries relevant information across many time steps without being overwritten.

12. What is LSTM model used for?

LSTM models provide an ideal model for tasks where order matters and context matters, such as sequence prediction and learning patterns.

13. Where is LSTM used in real-world applications?

LSTM is widely used in speech recognition, text processing, time-series forecasting, recommendation systems, and anomaly detection.

14. What is the difference between LSTM and GRU?

GRU is a simplified version of LSTM with fewer gates, making it faster, while LSTM offers more control over memory handling.

15. Is LSTM a supervised or unsupervised algorithm?

LSTM machine learning models are usually trained in a supervised manner, though they can also be adapted for unsupervised tasks.

16. How is LSTM used in time series forecasting?

LSTM learns patterns from historical data to predict future trends in sequential data such as stock prices or sensor readings.

17. Can LSTM be used for NLP tasks?

Yes, LSTM in machine learning is commonly used for NLP tasks such as language modeling, text classification, and sentiment analysis.

18. What are the limitations of LSTM networks?

LSTM models are computationally expensive, slower to train, and less efficient than Transformers on very large datasets.