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Applying Convolutional Neural Network with Candlestick Images to Predict Corn Price Movement
Author(s)
Date Issued
January 1, 2025
Type
Conference Paper
Abstract
The objective of this research is to study the effect of hyperparameters on corn price movement prediction models, namely batch size and learning rate, and create a model to predict the corn price movement in the Chicago Board of Trade (CBOT) based on candlestick images at 5-day and 20-day timeframes. The data are split into three sets, namely, training set, validation set, and test set, with a ratio of 70:10:20. The models presented in this research are CNN, VGG-16, and Efficientnet-B0, which must be fine-tuned. The study’s findings on hyperparameter values within a 5-day timeframe revealed that the optimal batch size and learning rates for all three models were a batch size of 16 with a learning rate of 0.001 and a timeframe of 20 days with a dataset size of 16. However, the suitable learning rate for the CNN model was 0.001, while for the VGG-16 and EfficientNet-B0 models, it was 0.0001. Subsequently, the hyperparameter values were fine-tuned for each model and tested the model with the test set. The study findings revealed that at the 5-day timeframe, the customized CNN model outperformed other models in predicting corn price movement, with an accuracy of 55.39%, while at a 20- day timeframe, the model with the highest accuracy was EfficientNet-B0, with an accuracy of 55.03%.
Citation
Lecture Notes in Networks and Systems, 1180 LNNS, 401-409, 2025