🤓LSTM Networks: The Power of Long-Short-Term Memory and comparisons to ARIMA/XGBoost/Prophet
"As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality." - Albert Einstein
🤔 What are "LSTM Networks"?
Explain it like I'm a CEO:
LSTM Networks are a type of artificial neural network used for processing sequential data. They can learn patterns in data and predict future values based on past information. LSTM Networks are particularly useful when it comes to processing time-series data, natural language processing, and speech recognition.
Why do I care about LSTM Networks?
As a CEO, you can apply LSTM Networks to predict future trends in your company's sales, customer demand, or inventory levels. By training the model on historical data, you can get a better understanding of how your business operates and make informed decisions to improve efficiency and profitability.
How can I apply LSTM Networks?
Suppose you're the CEO of an e-commerce company, and you want to predict the number of daily orders for the next month. You can use LSTM Networks to predict this value based on past data. By training the model on daily order data from the last year, the LSTM Network can learn the patterns in the data and predict the number of daily orders for the next month.
🤓 For the experts
Three principles to remember and master:
Long-Short-Term Memory: LSTM Networks can remember information for long periods, making them well suited for processing sequential data.
Gate Mechanisms: LSTM Networks use gates to control the flow of information, which allows them to selectively forget or remember information.
Vanishing Gradient Problem: LSTM Networks can suffer from the vanishing gradient problem, which can cause the model to have difficulty learning long-term dependencies. To address this issue, you can use gradient clipping or add skip connections.
📖 A bit of history
Sepp Hochreiter and Jürgen Schmidhuber introduced LSTM Networks in 1997 (original paper). They developed the concept while working on a project that involved predicting future values of a time series. LSTM Networks were an improvement over traditional recurrent neural networks (RNNs), which were prone to the vanishing gradient problem.
🐼 Data Science all the Things
Python Package: Keras
from keras.models import Sequential
from keras.layers import LSTM, Dense
model = Sequential()
model.add(LSTM(128, input_shape=(None, 1)))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='adam')
model.fit(X_train, y_train, batch_size=64, epochs=50)
This code shows how to create an LSTM Network using the Keras library. The model has one LSTM layer with 128 units, followed by a dense layer with one output.
R Package: torch
library(torch)
model <- nn$Sequential(
nn$LSTM(128, 1, batch_first=TRUE),
nn$Linear(1)
)
loss_fn <- nn$MSELoss()
optimizer <- optim$Adam(model$parameters(), lr=0.001)
for (epoch in 1:50) {
for (i in 1:n_batches) {
optimizer$zero_grad()
output <- model(X_batch)
loss <- loss_fn(output, y_batch)
loss$backward()
optimizer$step()
}
}
This code shows how to create an LSTM Network using the torch library in R. The model has one LSTM layer with 128 units, followed by a linear layer with one output.
Comparing to ARIMA/XGBoost/Prophet
This great post by Neptune AI compares LSTMs to ARIMA/XGBoost/Prophet for time series forecasting. Takeaway? At the end of the day you need to see what tool predicts best for your dataset.
One advantage of LSTMs is that they are capable of capturing long-term dependencies in time series data, which can be difficult for other models to do effectively. This is because LSTMs have a memory cell that allows them to selectively forget or remember past inputs based on their relevance to the current output. This makes them particularly well-suited to tasks like speech recognition, machine translation, and time series forecasting, where long-term dependencies are often present.
Another advantage of LSTMs is that they can handle a wide variety of input and output types, including continuous, binary, and categorical data. This makes them very flexible and able to handle a wide range of real-world data types.
However, one potential disadvantage of LSTMs is that they can be computationally expensive to train, especially if you have a large dataset with many time steps. They also require a lot of data to train effectively, so if you have a small dataset, other models like ARIMA or Prophet may be more appropriate.
In summary, LSTMs are a powerful tool for time series forecasting that offer several advantages over other models like XGBoost, ARIMA, or Prophet, particularly when dealing with long-term dependencies and a wide variety of input and output types. However, they can be computationally expensive to train and may require more data than other models to perform well.
🧠 Drop your Knowledge
If you know something about LSTM, share below in the comments!