TY  - JOUR
AU  - Arya, Suraj
AU  - Kasana, Singara Singh
PY  - 2026
DA  - 2026/03/23
TI  - An Analysis of Time Series Models for Predicting Global Rice Price
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 2
IS  - 3
SP  - 116
EP  - 126
DO  - 10.62762/TMI.2025.272892
UR  - https://www.icck.org/article/abs/TMI.2025.272892
KW  - machine learning
KW  - time series analysis
KW  - global rice price prediction
KW  - ARIMA
KW  - LSTM
KW  - BiLSTM
AB  - Rice plays a crucial role globally, as it is widely consumed across nations. Therefore, studying rice prices is vital, since fluctuations in the price can affect both its consumption and availability. This study analyzes time-series models using an international dataset. After preprocessing, the dataset comprises 71,856 samples and eight input features from six countries. The original dataset contained 300,816 rows and 23 columns. This study aims to predict rice inflation rates using time series models such as ARIMA, LSTM, and BiLSTM. The ARIMA model achieved the best combination of values (4,1,4)(0,0,0). Various statistical techniques that calculate inflation rates require expert knowledge and are time-consuming. However, with the advancement of intelligent computing and machine learning models, the rice inflation rate can now be predicted efficiently. These models play a vital role in managing unjustified surges in global rice prices.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Arya2026An,
  author = {Suraj Arya and Singara Singh Kasana},
  title = {An Analysis of Time Series Models for Predicting Global Rice Price},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {116-126},
  doi = {10.62762/TMI.2025.272892},
  url = {https://www.icck.org/article/abs/TMI.2025.272892},
  abstract = {Rice plays a crucial role globally, as it is widely consumed across nations. Therefore, studying rice prices is vital, since fluctuations in the price can affect both its consumption and availability. This study analyzes time-series models using an international dataset. After preprocessing, the dataset comprises 71,856 samples and eight input features from six countries. The original dataset contained 300,816 rows and 23 columns. This study aims to predict rice inflation rates using time series models such as ARIMA, LSTM, and BiLSTM. The ARIMA model achieved the best combination of values (4,1,4)(0,0,0). Various statistical techniques that calculate inflation rates require expert knowledge and are time-consuming. However, with the advancement of intelligent computing and machine learning models, the rice inflation rate can now be predicted efficiently. These models play a vital role in managing unjustified surges in global rice prices.},
  keywords = {machine learning, time series analysis, global rice price prediction, ARIMA, LSTM, BiLSTM},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}