A data scientist is trying to create a Neural Network that can predict and successfully trade stock prices based sole on its previous prices and/or reported financial metrics. What type of deep learning systems would be best suited for her purpose?AUse a generic artificial neural network ANNBUse a convolutional neural network CNNCUse a recurrent neural network RNNDUse a forward propagation neural network FNN
Question
A data scientist is trying to create a Neural Network that can predict and successfully trade stock prices based sole on its previous prices and/or reported financial metrics. What type of deep learning systems would be best suited for her purpose?AUse a generic artificial neural network ANNBUse a convolutional neural network CNNCUse a recurrent neural network RNNDUse a forward propagation neural network FNN
Solution
The best type of deep learning system for predicting and trading stock prices based on previous prices and/or reported financial metrics would be a Recurrent Neural Network (RNN). This is because RNNs are designed to recognize patterns in sequences of data, like time series data, which makes them well suited for predicting future stock prices based on past data. They can remember important things about the input they received, which allows them to be very precise in predicting what's coming next. This is crucial in stock price prediction since the previous price of a stock is crucial in predicting its future price.
Similar Questions
What is one of the primary applications of Convolutional Neural Networks (CNN)?a.Speech recognitionb.Image recognitionc.Natural language processingd.Financial forecasting
An automobile dealership wants to use historic car sales data to train a machine learning model. The model should predict the price of a pre-owned car based on its make, model, engine size, and mileage. What kind of machine learning model should the dealership use automated machine learning to create? ClassificationRegressionTime series forecasting
Which of the following is a common architecture used for sequence learning in deep learning? Convolutional Neural Networks (CNNs) Autoencoders Recurrent Neural Networks (RNNs) Generative Adversarial Networks (GANs)
Recurrent Artificial Neural NetworksRecurrent Artificial Neural Networks (RNNs) are a type of neural network architecture that is designed to handle sequential data by introducing connections between units in the network that form directed cycles. This cyclic structure allows information to persist over time and enables the network to exhibit dynamic temporal behavior.In contrast to feedforward neural networks, where information flows in one direction from input to output, RNNs have connections that loop back on themselves, allowing them to maintain an internal state or memory of previous inputs. This makes them well-suited for tasks that involve sequential data or time series, such as natural language processing, speech recognition, and time series prediction. The basic unit of an RNN is called a recurrent neuron or a recurrent unit.
What is a Recurrent Neural Network?1 pointA Neural Network that can recur to itself, and is proper for handling sequential dataAn infinite layered Neural Network which is proper for handling structured dataA special kind of Neural Network to predict weatherA Markovian model to handle temporal data
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.