Comparative Forecasting of Indonesian Stock Prices Using ARIMA and Support Vector Regression: A Statistical Learning Approach
Bibliographic record
Abstract
This study aims to compare the forecasting performance of Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) models in predicting the monthly stock prices of PT.Telkom Indonesia (TLKM), one of Indonesia's leading state-owned enterprises.Utilizing historical data from January 2018 to December 2022, the models are evaluated based on their forecasting precision using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) as investor-grade performance metrics.The ARIMA (2,1,2) model was selected through Box-Jenkins methodology, while the SVR was optimized using a linear kernel and tuned hyperparameters.The results demonstrate that ARIMA outperforms SVR in both MAPE (2.01%) and RMSE (64.38), indicating better adaptability to the structured linear patterns found in the stock price series.Assumption-based forecasting simulations, extended to the year 2026, further suggest ARIMA's relative stability, although future projections remain subject to structural and market uncertainties.The findings emphasize the continued relevance of classical statistical models in investment-grade forecasting, where investor-grade is defined as forecasting precision achieving MAPE values below 5%, supporting decision-support applications in emerging markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".