[PLUS] Developing a Long Short Term Memory (LSTM) AI Model for Bitcoin Price Prediction

Long Short Term Memory Models
Long Short-Term Memory (LSTM) units are a type of recurrent neural network (RNN) architecture, which has proven to be exceptionally effective at handling long-term dependencies in sequential data. Traditional RNNs have a built-in memory that enables them to remember and utilize information from previous inputs in their predictions. However, they tend to struggle with "remembering" information from many steps back, especially as sequences get long. LSTMs solve this problem with a unique internal structure composed of gates: the forget gate, input gate, and output gate. These gates collectively decide what information should be stored, updated, or discarded at each time step, allowing the network to selectively remember or forget information. As a result, LSTMs can learn and remember over long sequences and are less susceptible to the vanishing gradient problem, which is a common issue in traditional RNNs. This capability makes them a powerful tool for tasks like time series forecasting, natural language processing, and more. We will build an LSTM model to predict and forecast Bitcoin prices using a timeseries history of Bitcoin's price history.

Model in Layman's Terms
Imagine you're reading a long novel and trying to remember the key events and characters as the story unfolds. By the time you're halfway through, you might struggle to recall the details from the early chapters. The brain's way of retaining critical information while discarding the unnecessary bits is akin to the magic behind Long Short-Term Memory (LSTM) units.

LSTM is a special type of neural network, which is essentially a computing system inspired by the structure and function of the human brain. These networks are designed to recognize patterns over time or sequences, much like remembering the plot in a novel. Traditional neural networks can struggle with retaining older or 'distant' information in long sequences, just like you might struggle to recall the beginning of the novel as you continue reading.

However, LSTM has a unique design that allows it to remember critical details over long periods and forget less important ones, much like how we remember key events in a story. It achieves this using what we call "gates" - mechanisms that decide what information to store, use, or throw away at each moment. Think of these gates like filters or checkpoints in our memory, sieving through details and deciding what's worth remembering.

In essence, LSTM can be thought of as an enhanced memory system, making it a powerful tool for tasks that involve sequences, such as predicting stock prices over time or understanding the meaning of a sentence. This capability has made them a favorite in the world of machine learning, especially when it comes to processing long series of data or understanding language.

The Code
Our LSTM model downloads data from our Binance BTCUSDT timeseries and then creates the LSTM model. In our model development, we will use 90% of the data for training, and the remaining 10% of the series for testing the model (this is configurable in the script). After splitting the data for training and testing, we create new datasets that helps train the model. Essentially, we use the closing price of one time point to predict the next point in the series, so we will "shift" the data in such a way to create new arrays that capture this explicitly. Then we create the LSTM model itself with a lookback period of 1; which means we will predict the next period based on the previous period. At the end, we create a graph of the BTC price chart and graph the actual vs predicted price (from the model). We will also create a new Pandas dataframe to line up our actual Bitcoin prices vs our predictions and then create a new column for differences. Actual prices vs predicted prices and their corresponding differences are saved to a CSV file called Prediction_Differences_results.csv as the final step.

Model Scoring

Actual Prices vs Model Output & Differences

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