Artificial Intelligence at GrowAI
Long Short-Term Memory
Long Short-Term Memory (LSTM) models are a type of recurrent neural network designed to handle long-range dependencies in data sequences, making them particularly well-suited for time series forecasting. Unlike traditional feedforward neural networks, LSTMs can retain information over long periods, essential for tasks like predicting stock prices, weather patterns, or sales forecasts. By capturing patterns and trends in sequential data effectively, LSTM models are powerful tools in analyzing and predicting time-based phenomena with a high degree of accuracy.
LSTMs are one of the main ML algorithms we use to power our stock research tool. We take vast amounts of data and try different inputs, combinations, weights, manipulate layers, and back test it all to give an AI based model used to help you in your research and finding your next investment.
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To learn more about Machine learning and Artificial Intelligence, check out this free course that walks through the fundamental concepts, as well as the code used to build your own models.
At GrowAI, we harness the power of cutting-edge Artificial Intelligence and Machine Learning technologies to offer precise predictions for stock movements. Our advanced algorithms analyze vast amounts of data to provide you with valuable insights in a simple way, empowering you to make informed investment decisions.
AI and Machine Learning
AI is a powerful tool that can be used for predicting future stock prices and developing trading strategies. Machine Learning (ML) is a subset of AI that focuses on providing data as an input to a system, and the system processes that data to come up with an output. In the stock market, this can be a buy or sell signal. Machine learning algorithms can be trained to learn and improve their decision making and pattern recognition with minimal human intervention. This makes it an ideal candidate for many investors, trading firms, hedge funds, investment banks, and any other investment engines to use for trading stocks. There are many different types of machine learning algorithms and each one has its own cost and benefit.
We use Neural Networks and Deep Learning to train our machines for predicting stock market prices.
Neural Networks and Deep Learning for Stock Price Prediction
Neural networks are a fundamental concept in the field of deep learning, mimicking the human brain's structure to process complex data. These networks consist of layers of interconnected nodes that follow a 3 step process for processing information and deriving a solution. This process involves receiving input data, performing computations, and producing an output. Deep learning takes neural networks a step further where it is used for dealing with large datasets and computing outputs for complex tasks. Here is what a typical neural network looks like:
As you can see from the image, deep learning uses multiple hidden layers to perform further computations and come up with a more accurate output. Here at GrowAI, we specifically use Recurrent Neural Networks (RNNs), which are neural networks that are designed for analyzing sequential data, such as text or time-series.