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Recurrent Neural Networks (RNN)
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In the context of financial forecasting, how can Recurrent Neural Networks (RNNs) be effectively utilized for sequence prediction in business intelligence applications?

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A

By analyzing historical stock price data to predict future prices

B

By generating random financial data for simulation

C

By creating static financial reports without time dependency

D

By performing unsupervised clustering on customer data

Understanding the Answer

Let's break down why this is correct

RNNs keep a hidden state that stores past information. Other options are incorrect because A RNN learns from data; it does not produce random numbers; RNNs process sequences, not static summaries.

Key Concepts

sequence prediction
applications in finance
business intelligence
Topic

Recurrent Neural Networks (RNN)

Difficulty

hard level question

Cognitive Level

understand

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Definition
Definition

Recurrent neural networks, including LSTM and gated recurrent networks, have been widely used for sequence modeling and transduction tasks. These networks factor computation along symbol positions and generate hidden states sequentially, limiting parallelization and efficiency.

Topic Definition

Recurrent neural networks, including LSTM and gated recurrent networks, have been widely used for sequence modeling and transduction tasks. These networks factor computation along symbol positions and generate hidden states sequentially, limiting parallelization and efficiency.

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