• Phulen Mahato

In this work, a methodology has been proposed for the analysis & forecasting of time series data. The methodology involves collecting historical stock data, preprocessing the data such as handling of incomplete data, duplicate data, and incorrectly formatted data, dealing with outliers, data normalization to ensure all inputs are on a similar scale, splitting the dataset into training and testing data sets, organize the dataset into sequential pattern. Train the proposed model using training dataset. Finally we have used the test dataset to evaluate the performance of the model. Our proposed model produces better accuracy in prediction when compared to the similar work done in the same field.

Time series data analysis, data forecasting, training dataset, testing dataset, noise removal, data normalization, sequential pattern, Long short-term memory.

"TS Predictor - An Approach to Analyze and Forecast Time Series Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.b214-b218, November-2023, Available :http://www.jetir.org/papers/JETIR2311127.pdf

"TS Predictor - An Approach to Analyze and Forecast Time Series Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppb214-b218, November-2023, Available at : http://www.jetir.org/papers/JETIR2311127.pdf