Managing Partitioning Uncertainty and Transition Ambiguity in Fuzzy Time Series: A Robust IT2FCM-Markov Chain Approach
Bibliographic record
Abstract
Fuzzy Time Series (FTS) models are effective for handling uncertain and vague datasets, but their performance is often limited by subjective partitioning and ambiguous state transitions.Objectives: This study aims to address partitioning uncertainty caused by empirical fuzzifier parameters in Fuzzy C-Means (FCM) and resolve the one-to-many transition ambiguity in Fuzzy Logical Relationship Groups (FLRGs).We propose a novel hybrid model, IT2FCM-MC-FTS, which integrates Interval Type-2 Fuzzy C-Means (IT2FCM) with a first-order Markov Chain (MC).The methodology involves using IT2FCM to generate robust, data-driven interval partitions that account for noise, followed by the application of MC transition probabilities to provide logical weights for resolving complex relationship groups.The model was validated using ambient CO concentration data from Semarang, Indonesia.Experimental results show that the proposed model achieved an RMSE of 887.47 and a SMAPE of 17.86%, significantly outperforming traditional FTS, FCM-FTS, and FTS-MC models.By synergizing robust clustering with probabilistic inference, the IT2FCM-MC-FTS model provides a more reliable and accurate framework for time series forecasting (TSF) in volatile environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".