Time series prediction is the statistical technique for predicting future values of the time series variables based on historical datasets. Traditional machine learning models are difficult to predict based on future actions and states. Reinforcement learning with deep learning models is the most promising technique for time series data.
Deep reinforcement learning learns useful representations for complex problems with high dimensional real-time data using the trial and error method. Deep reinforcement learning model autonomously and simultaneously learns optimal patterns of representations and produces optimal predictions for time series data. Time-series predictions using deep reinforcement learning helps to discover the optimal strategies to act on time series data and provide fast and accurate predictions.