Applying Convolutional Neural Network with Candlestick Images to Predict Corn Price Movement
| dc.contributor.author | Chaiwuttisak, Pornpimol | |
| dc.date.accessioned | 2026-08-06T10:49:12Z | |
| dc.date.available | 2026-08-06T10:49:12Z | |
| dc.date.issued | 2025-01-01 | |
| dc.description.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%. | |
| dc.identifier.citation | Lecture Notes in Networks and Systems, 1180 LNNS, 401-409, 2025 | |
| dc.identifier.doi | 10.1007/978-981-97-9324-2_32 | |
| dc.identifier.issn | 23673370 | |
| dc.identifier.other | 2-s2.0-105016138043 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/16456 | |
| dc.source | Lecture Notes in Networks and Systems | |
| dc.subject | Candlestick | |
| dc.subject | Convolutional neural network | |
| dc.subject | Corn price movement | |
| dc.subject | Deep learning | |
| dc.subject | Hyperparameter | |
| dc.title | Applying Convolutional Neural Network with Candlestick Images to Predict Corn Price Movement | |
| dc.type | Conference Paper |