Deep Learning Techniques for the Evaluation of a Financial Time Series Forecasting Model

Financial time series forecasting is one of the most challenging problems in business and economics. Time series prediction has long been a prominent field of research because of the vast range of information that can be captured in time series. Deep learning approaches, such as automatically managing temporal variables like pattern and seasonality and automatically learning temporal connections, enable forecasts in time series. Analyzing financial time series to forecast future advancements has been challenging for many years. This study suggests the Deep learning-assisted Improved Financial Time Series Forecasting Model (DL-IFTSFM) to forecast future events and trends for client financial investment. The final performance of any system trained to function by analyzing historical data is greatly impacted by the division of data into training, validation, and test sets. This notion is supported by data on the temporal structure of the financial industry, which shows that the fundamental relationship between predictions and reactions varies with time. This paper describes various practical aspects of deep learning in economic time series forecasting, including choosing the best models and setting hyperparameter values. The suggested DL-IFTSFM model outperforms the current models in terms of prediction accuracy (97.2%), recall (97.9%), F-measure (96.2%), performance (98.9%), and error rate (10.2%), according to numerical results.