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ISSN 2063-5346
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IMPROVED ACCURACY FOR FUTURE STOCK MARKET PREDICTION USING LSTM MODEL COMPARED WITH PROPHET

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R.Rohith Kumar, Gayathri
» doi: 10.31838/ecb/2023.12.sa1.443

Abstract

Aim: The objective of the work is to predict the Stock Price Prediction Using LSTM Model Compared with SVM To achieve accuracy a novel SV Classifier is used, Method and Materials: Accuracy and loss are performed with a DATA dataset from the keras library. The total sample size is 20. The two groups Convolutional linear regression (N=10) and Support Vector Machine algorithms (N=10). Result: The result proved that Support Vector Machine (SVM) with better accuracy of 97% than linear regression accuracy of 96%.p=0.04 (p<0.05 ) it is statistically significant with a pretest power of 80% .The two algorithms LSTM and SVM are statistically satisfied with the independent sample T-Test value (p<0.001) with confidence level of 95%. Conclusion: prediction of stock price significantly seems to be better in LSTM

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