题目
题目
多项选择题

Imagine you are crafting a deep learning tool for autonomous trading in the financial markets. You're contemplating the adoption of a Recurrent Neural Network (RNN) to decipher and forecast the patterns in stock prices over time. Evaluate the statements below to determine their truthfulness in the context of RNN’s design and its training regimen.

选项
A.(A) The construction of an RNN is inherently adaptive to sequences, enabling it to effectively manage and interpret the sequential dynamics present in time-series data, such as stock market prices.
B.(B) Opting for a more extensive or denser network configuration should be a strategic decision based on the intricacy and specific traits of the time-series data in question, not solely on the length of the data sequence.
C.(C) RNNs are adept at capturing and assimilating the time-dependent relationships within data, which is advantageous for identifying recurring temporal motifs in stock market trends.
D.(D) The process of data preprocessing and the transformation of features is a required phase in preparing stock price data for an RNN model, as it can illuminate key tendencies and features that may be hidden in the unprocessed data.
E.(E) The Backpropagation Through Time (BPTT) method is an integral part of the training phase for RNNs, permitting the network to adjust its parameters in a manner that respects the chronological order of the data series.
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思路分析
When evaluating statements about RNN design and training for time-series tasks like stock prices, each option touches on a common principle of recurrent networks and their practical use. Option (A) A statement that the construction of an RNN is inherently adaptive to sequences, enabling it to effectively manage and interpret sequential dynamics in time-series data: this is a foundational feature of RNNs. By maintaining a hidden state that evolves as new inputs arrive, RNNs capture dependencies across time steps, making them well-suited for sequential data such as stock prices where past b......Login to view full explanation

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