• ANUKEERTHI R
• Sai Venkat A

Advancing inventory rate predictions, the "TimeSeries Transformer" employs the Transformer model included with a Time2Vector layer, adept at shooting lengthyterm dependencies in monetary time collection statistics. This challenge utilizes normalized percentage changes of Intel Corporation’s inventory statistics, ensuring robustness across diverse market situations. Extensive data visualization aids in uncovering underlying trends, even as sequential records segmentation prepares the model for correct forecasting. The model's structure is quality-tuned via meticulous hyperparameter selection, emphasizing adaptability and precision in predictions. Comparative analyses reveal its superiority over traditional techniques, substantially upgrading predictive accuracy and responsiveness to market dynamics. This novel approach now not handiest enhances inventory marketplace analysis but also units a precedent for destiny predictive models in finance.

TimeSeries Transformer, Time2Vector, financial time series, predictive accuracy, hyperparameter optimization, market dynamics.

"TimeSeries Transformer: Advancing Stock Price Predictions with Time2Vector Technology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.p557-p570, April-2024, Available :http://www.jetir.org/papers/JETIR2404G73.pdf

"TimeSeries Transformer: Advancing Stock Price Predictions with Time2Vector Technology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppp557-p570, April-2024, Available at : http://www.jetir.org/papers/JETIR2404G73.pdf