Time series data is used in several cutting-edge and innovative technological industries, including finance, economics, meteorology, supply chain management, and health care. Accurate forecasting of time series data is essential for making successful decisions and adequately planning for the future. Deep learning technology has introduced novel dimensions for detecting patterns and establishing associations within time series data. Several time series forecasting techniques and their practical applications have been explored in open-loop forecasting. This technique anticipates the subsequent time step in a sequence solely based on input data. In this paper, a comprehensive solution is presented for implementing open-loop forecasting, encompassing key stages such as initialization, prediction, and visualization. Furthermore, it delineates the simulation and experimental configuration encompassing Adam optimization, data padding, data shuffling, and visualization. Also, it examines the open-loop forecasting approach, focusing on its merits in real-time forecasting situations when actual values are known before making forecasts. Thus, it enhances our knowledge of time series forecasting techniques and their use in real-world scenarios to promote sustainable solutions.