Applied LSTM: Use Cases, Types, and Challenges

Imagine asking Siri or Google Assistant to set a reminder for tomorrow. 

These speech recognition or voice assistant systems must accurately remember your request to set the reminder. 

Traditional recurrent networks like backpropagation through time (BPTT) or real-time recurrent learning (RTRL) struggle to remember long sequences because error signals can either grow too big (explode) or shrink too much (vanish) as they move backward through time. This makes learning from a long-term context difficult or unstable. 

Long short-term memory or LSTM networks solve this problem. 

This artificial neural network type uses internal memory cells to consistently flow important information, allowing machine translation or speech recognition models to remember key details for longer without losing context or becoming unstable.

Invented in 1997 by Sepp Hochreiter and Jürgen Schmidhuber, LSTM addresses RNNs’ inability to predict words from long-term memory. As a solution, the gates in an LSTM architecture use memory cells to capture long-term and short-term memory. They regulate the information flow in and out of the memory cell. 

Because of this, users don’t experience gradient exploding and vanishing, which usually occurs in standard RNNs. That’s why LSTM is ideal for natural language processing (NLP), language translation, speech recognition, and time series forecasting tasks. 

Let’s look at the different components of the LSTM architecture. 

LSTM architecture

The LSTM architecture uses three gates, input, forget, and output, to help the memory cell decide and control what memory to store, remove, and send out. These gates work together to manage the flow of information effectively.

• The input gate controls what information to add to the memory cell.
• The forget gate decides what information to remove from the memory cell.
• The output gate picks the output from the memory cell.

This structure makes it easier to capture long-term dependencies. 

Source: ResearchGate

Input gate

The input gate decides what information to retain and pass to the memory cell based on the previous output and current sensor measurement data. It’s responsible for adding useful information to the cell state. 

The input gate uses the sigmoid function to control and filter values to remember. It creates a vector using the tanh function, which produces outputs ranging from -1 to +1 that contain all potential values between h_t-1 and x_t. Then, the formula multiplies the vector and regulated values to retain valuable information. 

Finally, the equation multiplies the previous cell state element-wise with the forget gate and forgets values close to 0. The input gate then determines which new information from the current input to add to the cell state, using the candidate cell state to identify potential values.

Forget gate

The forget gate controls a memory cell’s self-recurrent link to forget previous states and prioritize what needs attention. It uses the sigmoid function to decide what information to remember and forget. 

The forget gate formula shows how a forget gate uses a sigmoid function on the previous cell output (h_t-1) and the input at a particular time (x_t). It multiplies the weight matrix with the last hidden state and the current input and adds a bias term. Then, the gate passes the current input and hidden state data through the sigmoid function. 

The activation function output ranges between 0 and 1 to decide if part of the old output is necessary, with values closer to 1 indicating importance. The cell later uses the output of f(t) for point-by-point multiplication.

Output gate

The output gate extracts useful information from the current cell state to decide which information to use for the LSTM’s output. 

It generates a vector by using the tanh function on the cell. Then, the sigmoid function regulates the information and filters the values to be remembered using inputs h_t-1 and x_t. Finally, the equation multiplies the vector values with regulated values to produce and send an input and output to the next cell.

Hidden state 

On the other hand, the LSTM’s hidden state serves as the network’s short-term memory. The network refreshes the hidden state using the input, the current state of the memory cell, and the previous hidden state. 

Unlike the hidden Markov model (HMM), which predetermines a finite number of states, LSTMs update hidden states based on memory. This hidden state’s memory retention ability helps LSTMs overcome long-time lags and tackle noise, distributed representations, and continuous values. That’s how LSTM keeps the training model unaltered while providing parameters like learning rates and input and output biases.

Next up: a closer look at the different forms LSTM networks can take.

Types of LSTM recurrent neural networks

There are X variations of LSTM networks, each with minor changes to the basic architecture to address specific challenges or improve performance. Let’s explore what they are.

1. Classic LSTM

Also known as vanilla LSTM, the classic LSTM is the foundational model Hochreiter and Schmidhuber promised in 1997. 

This model's RNN architecture features memory cells, input gates, output gates, and forget gates to capture and remember sequential data patterns for longer periods. This variation’s ability to model long-range dependencies makes it ideal for time series forecasting, text generation, and language modeling.

2. Bidirectional LSTM (BiLSTM)

This RNN’s name comes from its ability to process sequential data in both directions, forward and backward. 

Bidirectional LSTMs involve two LSTM networks — one for processing input sequences in the forward direction and another in the backward direction. The LSTM then combines both outputs to produce the final result. Unlike traditional LSTMs, bidirectional LSTMs can quickly learn longer-range dependencies in sequential data. 

BiLSTMs are used for speech recognition and natural language processing tasks like machine translation and sentiment analysis. 

3. Gated recurrent unit (GRU)

A GRU is a type of RNN architecture that combines a traditional LSTM’s input gate and forget fate into a single update gate. It earmarks cell state positions to match forgetting with new data entry points. Moreover, GRUs also combine cell state and hidden output into a single hidden layer. As a result, they require less computational resources than traditional LSTMs because of the simple architecture. 

GRUs are popular in real-time processing and low-latency applications that need faster training. Examples include real-time language translation, lightweight time-series analysis, and speech recognition. 

4. Convolutional LSTM (ConvLSTM)

Convolutional LSTM is a hybrid neural network architecture that combines LSTM and convolutional neural networks (CNN) to process temporal and spatial data sequences.

It uses convolutional operations within LSTM cells instead of fully connected layers. As a result, it’s better able to learn spatial hierarchies and abstract representations in dynamic sequences while capturing long-term dependencies. 

Convolutional LSTM’s ability to model complex spatiotemporal dependencies makes it ideal for computer vision applications, video prediction, environmental prediction, object tracking, and action recognition. 

5. LSTM with attention mechanism 

LSTMs using attention mechanisms in their architecture are known as LSTMs with attention mechanisms or attention-based LSTMs.

Attention in machine learning occurs when a model uses attention weights to focus on specific data elements at a given time step. The model dynamically adjusts these weights based on each element’s relevance to the current prediction. 

This LSTM variant focuses on hidden state outputs to capture fine details and interpret results better. Attention-based LSTMs are ideal for tasks like machine translation, where accurate sequence alignment and strong contextual understanding are crucial. Other popular applications include image captioning and sentiment analysis.

6. Peephole LSTM

A peephole LSTM is another LSTM architecture variant in which input, output, and forget gates use direct connections or peepholes to consider the cell state besides the hidden state while making decisions. This direct access to the cell state enables these LSTMs to make informed decisions about what data to store, forget, and share as output.

Peephole LSTMs are suitable for applications that must learn complex patterns and control the information flow within a network. Examples include summary extraction, wind speed precision, smart grid theft detection, and electricity load prediction. 

LSTM vs. RNN vs. gated RNN

Recurrent neural networks process sequential data, like speech, text, and time series data, using hidden states to retain past inputs. However, RNNs struggle to remember long sequences from several seconds earlier due to vanishing and exploding gradient problems.

LSTMs and gated RNNs address the limitations of traditional RNNs with gating mechanisms that can easily handle long-term dependencies. Gated RNNs use the reset gate and update gate to control the flow of information within the network. And LSTMs use input, forget, and output gates to capture long-term dependencies. 

LSTM applications

LSTM models are ideal for sequential data processing applications like language modeling, speech recognition, machine translation, time series forecasting, and anomaly detection. Let’s look at a few of these applications in detail.

• Text generation or language modeling involves learning from existing text and predicting the next word in sequences based on contextual understanding of the previous words. Once you train LSTM models on articles or coding, they can help you with automatic code generation or writing human-like text. 
• Machine translation uses AI to translate text from one language to another. It involves mapping a sequence in a language to a sequence in another language. Users can use an encoder-decoder LSTM model to encode the input sequence to a context vector and share translated outputs.
• Speech recognition systems use LSTM models to process sequential audio frames and understand the dependencies between phonemes. You can also train the model to focus on meaningful parts and avoid gaps between important phonetic components. Ultimately, the LSTM processes inputs using past and future contexts to generate the desired results.
• Time series forecasting tasks also benefit from LSTMs, which may sometimes outperform exponential smoothing or autoregressive integrated moving average (ARIMA) models. Depending on your training data, you can use LSTMs for a wide range of tasks. 

For instance, they can forecast stock prices and market trends by analyzing historical data and periodic pattern changes. LSTMs also excel in weather forecasting, using past weather data to predict future conditions more accurately. 

• Anomaly detection applications rely on LSTM autoencoders to identify unusual data patterns and behaviors. In this case, the model trains on normal time series data and can’t reconstruct patterns when it encounters anomalous data in the network. The higher the reconstruction error the autoencoder returns, the higher the chances of an anomaly. This is why LSTM models are widely used in fraud detection, cybersecurity, and predictive maintenance. 

Organizations also use LSTM models for image processing, video analysis, recommendation engines, autonomous driving, and robot control.

Drawbacks of LSTM

Despite having many advantages, LSTMs suffer from different challenges because of their computational complexity, memory-intensive nature, and training time.

• Complex architecture: Unlike traditional RNNs, LSTMs are complex as they deal with multiple gates for managing information flow. This complexity means some organizations may find implementing and optimizing LSMNs challenging. 

• Overfitting: LSTMs are prone to overfitting, meaning they may end up generalizing new, unseen data despite being trained well on training data, including noise and outliers. This overfitting happens because the model tries to memorize and match the training data set instead of actually learning from it. Organizations must adopt dropout or regularization techniques to avoid overfitting. 

• Parameter tuning: Tuning LSTM hyperparameters, like learning rate, batch size, number of layers, and units per layer, is time-consuming and requires domain knowledge. You won’t be able to improve the model’s generalization without finding the optimal configuration for these parameters. That’s why using trial and error, grid search, or Bayesian optimization is vital to tune these parameters. 

• Lengthy training time: LSTMs involve multiple gates and memory cells. This complexity means you must train the model for many computations, making the training process resource-intensive. Plus, LSTMs need large datasets to learn how to adjust weights for loss minimization iteratively, another reason training takes longer. 
• Interpretability challenges: Many consider LSTMs as black boxes, meaning it’s difficult to interpret how LSTMs make predictions based on various parameters and their complex architecture. Unlike traditional RNNs, you can’t trace back the reasoning behind predictions, which may be crucial in industries like finance or healthcare. 

Despite these challenges, LSTMs remain the go-to choice for tech companies, data scientists, and ML engineers looking to handle sequential data and temporal patterns where long-term dependencies matter.

Next time you ask Siri or Alexa, thank LSTM for the magic 

Next time you chat with Siri or Alexa, remember: LSTMs are the real MVPs behind the scenes. 

They help you overcome the challenges of traditional RNNs and retain critical information. LSTM models tackle information decay with memory cells and gates, both crucial for maintaining a hidden state that captures and remembers relevant details over time. 

While already foundational in speech recognition and machine translation, LSTMs are increasingly paired with models like XGBoost or Random Forests for smarter forecasting. 

With transfer learning and hybrid architectures gaining traction, LSTMs continue to evolve as versatile building blocks in modern AI stacks.

As more teams look for models that balance long-term context with scalable training, LSTMs quietly ride the wave from enterprise ML pipelines to the next generation of conversational AI.

Looking to use LSTM to get helpful information from massive unstructured documents? Get started with this guide on named entity recognition (NER) to get the basics right. 

Edited by Supanna Das