The Prediction Process Based on Deep Recurrent Neural Networks: A Review

Abstract

Prediction is vital in our daily lives, as it is used in various ways, such as learning, adapting, predicting, and classifying. The prediction of parameters capacity of RNNs is very high; it provides more accurate results than the conventional statistical methods for prediction. The impact of a hierarchy of recurrent neural networks on Predicting process is studied in this paper. A recurrent network takes the hidden state of the previous layer as input and generates as output the hidden state of the current layer. Some of deep Learning algorithms can be utilized in as prediction tools in video analysis, musical information retrieval and time series applications. Recurrent networks may process examples simultaneously, maintaining a state or memory that recreates an arbitrarily long background window. Long Short-Term Memory (LSTM) and Bidirectional RNN (BRNN) are examples of recurrent networks. This paper aims to give a comprehensive assessment of predictions based on RNN. Additionally, each paper presents all relevant facts, such as dataset, method, architecture, and the accuracy of the predictions they deliver.

Haze Prediction Model Using Deep Recurrent Neural Network

“…Deep recurrent neural networks have many advantages, with stronger parameter prediction power than other neural networks and higher accuracy in the prediction results. RNN and long-term memory prediction models are examples of recurrent networks that take the hidden state of the previous layer as input and generate the hidden state of the current layer as output, improving its prediction accuracy and a larger processing range of data [34]. Compared with general models with ordinary structures, the dynamic properties of the data changing with time series are fully considered [35].…”