Virual Oral Presentation Slide by Slide

IEEE/ICACT20250232 Slide.06        [Big Slide]	Oral Presentation
	mplementation DetailsL Individual model details: LSTM, or Long Short-Term Memory, excels at capturing long-term dependencies in time series data. LSTMs can learn complex sequential patterns and are particularly well-suited for data with clear temporal dependencies. Next, Neural Prophet, a modern neural network-based extension of the popular Prophet model, was incorporated. Neural Prophet handles multiple seasonalities effectively and is adept at capturing the impact of holidays and special events. It offers greater flexibility compared to the traditional Prophet model. Finally, XGBoost, an ensemble learning method, was included. XGBoost is known for its ability to capture non-linear relationships in data. It is less prone to overfitting due to its regularization techniques. XGBoost also boasts fast training and prediction times and works well with engineered time features.

[Go to Next Slide]

Select Voice: Virtual Oral Presentation!!