Recurrent Neural Networks for Efficient Solutions to Time-Series Forecasting in Complex Mathematical Models

Time series Forecasting plays a major role mainly in areas including finance, health, and meteorology. Although, classical forecast methods, including ARIMA models and feedforward neural networks, fail at capturing long-term information and non-linear interactions in temporal systems. This paper explores the application of LSTM networks in improving forecasting accuracy while overcoming the limitations of traditional methods. The contribution of this work is in applying of LSTM models into complex mathematical frameworks capable to work with time-series data with minimal efforts required in feature engineering. A plan of the proposed framework is as follows: LSTM model training on the given time-series data and comparison with ARIMA and feedforward neural networks that leads to the comparison of results based on performance metrics like MAE, RMSE and MAPE. Overall, out of all analysed methods, LSTM model provides the lowest values of mean absolute error equal to 8.12, root mean squared error equal to 10.23 and mean absolute percentage error equal to 5.29 % which proves how good it was in dealing with data which contains relations with a more non-linear nature and experiencing long dependencies. Such results suggest that LSTMs are significantly superior to traditional approaches as a new method of time-series analysis and forecasting for complex systems. It is important to discuss the advantages of LSTM based on the aspect of the capability of being scaled and trainable when it comes to time-series forecasting and its demanding computation resource and training data.