ANN and SVM, both are Machine Learning techniques. What are your views regarding when would you choose ANN over SVM, and vice versa, for forecasting the stock prices?
Both ANN and SVM have their place in stock price forecasting, depending on the nature of the data and prediction goal. I would choose ANN (including LSTM or DNN variants) when working with nonlinear time-series data that exhibits strong temporal dependencies, as ANN can learn complex patterns over time. On the other hand, SVM is preferred when the dataset is smaller, less noisy, or requires robust generalization—especially in high-dimensional but non-temporal datasets.
In my paper, “Dynamic Stock Price Prediction using LSTM, ARIMA, and Sparrow Search Algorithm”, we evaluated different predictive models and found deep learning (ANN + optimization) offered better performance on volatile and sequential stock data. SVM performed better only under limited, denoised input features. So the choice depends on data volume, noise, and time dependency.
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Article Dynamic Stock Price Prediction Leveraging LSTM, ARIMA, and S...
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