Next Sequence Number Prediction in Natural Languages using Deep Learning

Natural languages Generation mainly focus on generating human interpretable languages This study suggests an innovative method to guess the following sequence number and user's keystrokes can be minimized by anticipating the subsequent sequence. For the job of guessing the next number, two deep learning techniques Long Short Term Memory and Bi-LSTM (Bi-directional-LSTM) have been investigated. This method perhaps utilized with a range of NLG(Natural Language Generation) tasks, including sentence and auto completion. A key problem in many fields, from time series analysis to natural language processing, is the prediction of sequential data. LSTM setups have shown to be successful at identifying patterns in such data and modeling sequence dependencies.  Paper will investigate the use of LSTM networks for forecasting future numerical sequences. The experiment with various sequence lengths, network designs, and hyper parameters to show the efficacy of our method. The predictive power of the LSTM model with that of standard techniques like moving averages and autoregressive algorithms. This method also examine the model's sensitivity to noisy or irregular data as well as its ability to generalize to various kinds of numerical sequences.