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@article{205031,
        author = {Mrunali.R.Pol and Mrudula.R.Pol and G.S.Salokhe and S.M.Bhori},
        title = {A Review Paper On Deep Learning Approaches For Time Series Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5604-5610},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205031},
        abstract = {Deep learning has become a transformative approach for time series forecasting by effectively capturing complex temporal patterns beyond traditional methods. This paper integrates concepts from differential equations and statistical models with advanced deep learning architectures such as LSTM and transformer networks. The proposed framework models dynamic system behaviour while leveraging data-driven learning for improved prediction accuracy. Applications in stock price prediction and weather forecasting are explored to demonstrate real-world relevance. Experimental results show that deep learning models outperform conventional statistical techniques in handling nonlinearity and large-scale data, offering robust and scalable solutions for accurate time series prediction across diverse domains.},
        keywords = {Time Series Forecasting, Deep Learning, LSTM, Recurrent Neural Networks (RNN), Transformer Models, Differential Equations, Statistical Models, Stock Price Prediction, Weather Forecasting, Predictive Analytics, Temporal Data Analysis, Nonlinear Systems},
        month = {June},
        }