Application of the Cheng Fuzzy Time Series Model for Stock Price Forecasting: A Case Study in the Energy Sector
Bibliographic record
Abstract
This study investigates the application of the Cheng Fuzzy Time Series (FTS) model in forecasting stock prices, using PT Bukit Asam Tbk (PTBA) as a case study in the energy sector.Unlike traditional models, Cheng FTS leverages fuzzy logic and linguistic rules to model uncertainty in financial data.Weekly closing prices from January 2020 to December 2022 were used, with a 70-30 data partitioning for training and testing.The universe of discourse was constructed using a buffered range and divided into optimized fuzzy intervals to define linguistic states.The Cheng FTS model was benchmarked against the Autoregressive Integrated Moving Average (ARIMA) model to assess predictive accuracy.Forecasting performance was evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), with the Cheng FTS achieving a MAPE of 4.93% and RMSE of 128.6 IDR.Results show that the model effectively captures price trends in stable periods, though its rule-based structure limits responsiveness during high volatility.This study demonstrates the practical value of interpretable fuzzy models for medium-term financial forecasting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".