A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction

Jianzhou Wang; Jinze Li; Yijun Geng; Zhiwu Li

REVIEW 6 major objections 5 minor 12 references

A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction

T0 review · 6 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read CGT-BF, built on fuzzy-set feature extraction and a weight-optimized ensemble, improves short-term wind speed point forecasts by an average of 21.6% and outperforms eight comparison models on most point and interval metrics.

desk verdict A plausible hybrid wind-speed forecasting system, but the windowing and interval construction are under-specified, the headline 21.6% gain does not match the paper's own table, and the reported statistics contain internal contradictions. read the letter →

arxiv 2507.05761 v1 pith:MKM6MIXL submitted 2025-07-08 stat.AP

Yijun Geng , Jianzhou Wang , Jinze Li , Zhiwu Li This is my paper · ORCID

classification stat.AP MSC 62M2062H30

keywords windspeedpredictionfuzzyinformationgranulationroughsetfeatureextractionensemblelearningmulti-objectiveoptimizationintervalshort-termforecasting

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is trying to establish that a wind speed forecasting system called CGT-BF can make point and interval forecasts that are substantially more accurate than eight alternatives, with an average point-prediction improvement of 21.6% on MAPE. The system replaces raw wind speed inputs with fuzzy-set features extracted from 36-point windows, then combines four machine-learning predictors whose weights are tuned by a multi-objective optimizer. Three datasets from the Penglai wind farm, five-fold cross-validation, DM significance tests, and interval-coverage metrics are used to support the claim. If the results hold, short-term wind power dispatch and turbine control could be planned from more reliable forecasts without collecting additional meteorological variables.

What carries the argument

The load-bearing mechanism is the FIC-MG feature extractor. A sliding window of 36 ten-minute observations is fuzzified with a triangular membership function that produces three granule parameters, $Low$, $R$, and $Up$, which serve as initial cluster centers. Fuzzy rough C-means clustering then assigns points to upper approximation sets, lower approximation sets, or boundary regions using thresholds $r_1=0.3$ and $r_2=0.7$, and the cluster centers are updated from the membership matrix to yield the final features. The second mechanism is the T-BF ensemble: BiLSTM, CNN-GRU, LSTM-XGB, and random forest each predict from the fuzzy features, and IMOSFO, a sunflower optimizer initialized with tent mapping and an adaptive t-distribution, sets the ensemble weights by minimizing MAPE and MSE together. Interval forecasts come from the same system, with confidence intervals evaluated by PICP, PINAW, and AIS.

What would settle it

Re-run the Penglai wind farm comparison under walk-forward validation, recomputing the 36-point granulation window and all FIC-MG clustering parameters using only past observations at each prediction step; if CGT-BF’s MAPE advantage over the eight baselines disappears or drops below the reported 21.6%, the headline improvement is an artifact of information leakage rather than a real forecasting gain.

Watch

Extended reading notes

Core claim

The central claim is that CGT-BF, an integrated multiframe system, achieves better short-term wind speed prediction than single models (BiLSTM, CNN-GRU, LSTM-XGB, random forest) and combined models (MODA, MSSA, NSMFO, MOGWO). The gain comes from the FIC-MG preprocessing module, which fuzzifies each 36-point granulation window into Low, R, and Up parameters, performs fuzzy rough C-means clustering with upper and lower approximation sets, and updates cluster centers to produce optimal feature values; the T-BF ensemble then predicts those features with four learners whose weights are optimized by IMOSFO under a dual MAPE/MSE objective. The paper reports that the proposed system outperforms the baselines in most point and interval metrics, with an average point-prediction improvement of 21.6%, and that the point-forecast differences are positive in Diebold-Mariano tests.

Load-bearing premise

The 36-point fuzzy granulation window for a predicted time step must contain only observations at or before that step; if the window is centered or fitted on the full dataset including the test period, the reported 21.6% improvement could be inflated by information leakage.

Editorial extensions

If this is right

On short-term 10-minute-ahead forecasts, CGT-BF reduces MAPE by an average of 21.6% relative to the eight baselines, which would translate into smaller prediction errors for wind-farm scheduling.
At 95% and 85% confidence levels, the interval predictions improve on PICP and AIS on most datasets, giving grid operators a narrower band that still covers observed wind speeds.
Because five-fold cross-validation keeps MAPE near 4.1–4.8 and $R^2$ near 0.98–0.99 across folds, the accuracy gain is not an artifact of one train/test split.
Positive DM test statistics against all eight comparison models on MAPE indicate the point-forecast differences are reported as significant at the 5% level.
IMOSFO’s Pareto fronts on the ZDT1–ZDT3 benchmark functions are claimed to dominate those of MSSA and MODA, supporting the weight-update module used inside CGT-BF.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

As an extension beyond the paper, the same FIC-MG front end should transfer to other noisy renewable series, such as solar irradiance or wave height, by retuning only the window size and the two approximation thresholds.
The dual MAPE/MSE objective implies an operator could tune the system toward smoothness or peak accuracy; the fixed weights selected here are only one point on that trade-off frontier.
The reported gains are one-step 10-minute forecasts from one wind farm, so they should not be extrapolated to multistep or multi-site horizons until tested.
A stricter check of the temporal alignment of the 36-point windows would determine whether the 21.6% figure survives when granulation is recomputed on past data only; the paper does not state this alignment explicitly.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a routine hybrid forecasting paper, and the new preprocessing block (FIC-MG) is an incremental arrangement of known pieces. The paper isn't empty—three real datasets, a 60/20/20 split, five-fold CV, eight comparisons, and a clear lineage from the corresponding author's earlier load-forecasting work. I believe the point-forecast results could be roughly right if the preprocessing is done honestly. But the manuscript as written does not let you verify that, and there are several internal inconsistencies that push me to reject.

The biggest problem is exactly what the stress-test flagged. Section 2.1 gives a window size of 36 but never says whether the window that produces the feature for time t is [t-35, t] or something centered. Section 2.2 estimates cluster centers and the granulation parameters "over the dataset T" with no statement that this is restricted to the training 60%. So the 21.6% average improvement—which I note is not reproducible from Table 8; the IRI entries average to about 15.9%—could be an artifact of look-ahead. This is a load-bearing ambiguity, not a style complaint.

There's more. The interval-prediction contribution has no derivation; there is no equation or algorithm in §2.5 that explains how PICP/PINAW/AIS are produced, yet it's claimed as a novel dual framework. The baselines' input features are never specified—do BiLSTM/CNN-GRU/etc. use raw wind speed or the same granulated features? If the former, the comparison conflates preprocessing and model. And the DM test section says all values are positive, but Table 7 contains negative DM statistics for MODA, NSMFO, and MOGWO on MSE in multiple datasets; negative values mean the proposed model is worse, contradicting the text. Table 2 also has at least one physically absurd PINAW entry (Data3 BILSTM: 0.399 at 85% confidence vs 0.054 at 95%), which looks like a data-entry error.

What does the paper do well? The intention to combine fuzzy granulation with rough-set clustering and an adaptive ensemble is coherent, and the five-fold CV in Experiment III is a reasonable stability check. If the authors supply code/data and clarify the windowing protocol, this could become a modest, citable contribution in the wind-speed-prediction literature. But as it stands, the central empirical claim is not independently verifiable, and the internal contradictions undermine confidence.

Who should read it: anyone who works on hybrid fuzzy/rough-set forecast preprocessing and wants to see the current state of that thread. I would not cite it in its present form. My recommendation to an editor: reject or, at most, send it back for major revision with an explicit request for a causal-windowing proof, a description of interval construction, and a correction of the DM/IRI reporting. The authors deserve a chance to fix it, but the burden of demonstration is entirely on them.

Referee Report

6 major / 5 minor

Summary. The paper proposes CGT-BF, a short-term wind speed prediction system that combines a fuzzy information granulation and fuzzy rough C-means feature extraction module (FIC-MG) with four machine-learning predictors (BiLSTM, CNN–GRU, LSTM–XGB, and RF), whose ensemble weights are optimized by a multi-objective sunflower algorithm (IMOSFO). The system is evaluated on three 10-minute wind speed series from the Penglai wind farm under a 60/20/20 train/validation/test split. The authors report point-forecast metrics (MAPE, MSE, R²), interval-forecast metrics (PICP, PINAW, AIS), Diebold–Mariano tests, five-fold cross-validation, and optimization benchmarks on ZDT functions, and they claim an average point-prediction improvement of 21.6% over eight comparison models.

Significance. If the reported protocol is sound, the idea of extracting granular fuzzy features before feeding them to a weighted ensemble of neural and tree models is a plausible contribution to short-term wind speed forecasting, and the paper supplies a fairly broad empirical comparison: three datasets, four single and four ensemble baselines, five-fold cross-validation, DM tests, and benchmark test functions. The explicit holdout split for point forecasting and the use of multiple error metrics are strengths, as is the attempt to provide both point and interval forecasts. However, the contribution's significance hinges on the temporal causality of the FIC-MG preprocessing and on the reproducibility of the interval framework, neither of which is currently documented. The absence of code or data makes these omissions especially consequential: the central numerical claims cannot be independently checked.

major comments (6)

[§2.1–§2.2] The temporal protocol of FIC-MG is not specified. The manuscript states only that "a window size of 36 was used" (§2.1) and that the granulation and fuzzy C-means operations are applied to "the dataset T" (§2.2, Eqs. 4–6), but it never states whether the 36-point window used for the feature at forecast time t is [t−35, t], centered, or future-inclusive, nor whether the granulation parameters, the rough-set thresholds r1/r2 = 0.3/0.7, and the cluster centers are estimated on the 60% training segment only. If the windows include future observations or the preprocessing is fitted on the full dataset including the final 20% test segment, the MAPE/MSE gains and the DM-test significance reported in Tables 2, 3, and 7 would be inflated by information leakage. The authors must specify a causal, training-only protocol and re-run the experiments if any leakage is found.
[§2.2] The FIC-MG feature construction is incomplete. The method produces per-window Low, R, and Up parameters and updated cluster centers, but the manuscript does not define how these objects are assembled into a predictor vector for a given forecast target, how many fuzzy features enter each learner, or whether the same feature matrix is used for all four learners. Without this specification, neither the baselines' input features nor the proposed model's inputs can be reconstructed, and the comparison in §3.1 is not reproducible.
[§3.1 and §3.2] The interval prediction framework is never described. Tables 2 and 3 report PICP, PINAW, and AIS for 95% and 85% confidence intervals, but Section 2 contains no equations or algorithmic steps for constructing the upper and lower bounds, and no statement about whether the intervals are calibrated on the validation set or the test set. The paper's 'dual-frame' claim (Introduction, item 2) therefore cannot be evaluated.
[§4.2, Eq. (28)] The reported DM statistic is not a standard Diebold–Mariano statistic. The formula as written is a raw sum of squared-error differences with no normalization by the standard deviation of the loss differential or by sample size; as a result the values 8–13 in Table 7 cannot be interpreted as standard-normal test statistics. Please provide the correct DM formula, the loss differential series, and p-values.
[§5 vs. Table 8] The claimed 'average point prediction improvement of 21.6%' is not supported by Table 8. Averaging the eight IRI entries per dataset gives approximately 15.1%, 19.7%, and 12.7% for Data1–Data3, with an overall average of roughly 15.9%; the 21.6% figure is not reproduced by any straightforward reading of the table. The conclusion should either report the exact computation or be corrected.
[§3.1] The baseline comparison is not fully defined because the inputs to the benchmark models are not specified. It is unclear whether BILSTM, CNN–GRU, LSTM–XGB, and RF are fed the raw wind speed lags, the FIC-MG features, or the granulated Low/R/Up values; the text only gives hyperparameters. This affects the fairness and interpretability of every point- and interval-forecast comparison in Tables 2 and 3.

minor comments (5)

[Throughout] Numerous equations are garbled or mis-rendered (e.g., Eqs. 3, 5, 6, 20–21, 24), with symbols missing or misplaced; the manuscript needs a careful typesetting pass before it can be assessed accurately.
[Table 2] In the Data2, CNN–GRU row, the PINAW values of 0.623 and 0.477 are implausible compared with all other entries (roughly 0.04–0.07) and appear to be typographical errors for 0.062 and 0.047.
[§3.1 and Table 2] The text says the baselines are BILSTM, CNN–GRU, LSTM–XGB, and RF, but Table 2 also lists GRU and XGBOOST; please clarify whether these are separate baselines or duplicates.
[§3.1 and Table 6] Terminology is inconsistent: 'Flod' in Table 4 should be 'Fold', and parameter names such as 'a sizeB', 'rateLearn', 'DepthMax', and 'trainMax' in §3.1 are not defined in Table 6.
[References] The reference list contains multiple 'Wang et al. (2023)' entries; the in-text citations should be disambiguated by author initials or reference numbers.

Circularity Check

0 steps flagged · score 2.0 of 10

No demonstrated circularity; the only self-citation (Wang et al. 2023 behind Eq. 6) is provenance, not a load-bearing unverified theorem.

full rationale

The CGT-BF derivation is essentially self-contained. FIC-MG's free constants (window size 36, upper/lower thresholds 0.7/0.3, three clustering centers) are fixed in Table 6 rather than fit to the target. The IMOSFO ensemble weights are optimized on the validation segment (60-80%) and the reported MAPE/MSE are measured on the held-out final 20%, so the point-prediction comparisons are out-of-sample rather than forced by construction. The IRI in Eq. (29) is an arithmetic ratio of reported MAPE values, so the claimed 21.6% improvement is a summary of test-set results, not a fitted parameter renamed as a prediction. The one notable self-citation is Eq. (6), whose cluster-center update is attributed to Wang et al. (2023) (Jianzhou Wang and Zhiwu Li are authors of both papers); however, the present paper states the full update formula and the cited work is an independently published power-load application, so the citation is provenance rather than a load-bearing external theorem. The ambiguity in Sections 2.1-2.2 about whether the 36-point granulation windows are causal and whether fuzzy C-means is fit on training data only is a genuine look-ahead risk, but the paper does not specify enough to exhibit the required equation-level reduction (feature at t equals a function of y_t), so under the hard rules it is a reproducibility concern rather than demonstrated circularity.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several user-chosen constants that are not justified by sensitivity analysis, most notably the 36-point window, the three cluster centers, and the rough-set thresholds 0.3 and 0.7. The ensemble weights are fitted to validation data but not reported. No new physical or mathematical entities are introduced. The main axioms are domain assumptions about the usefulness of fuzzy granulation and clustering for wind speed prediction.

free parameters (7)

Granulation window size = 36
Chosen without sensitivity analysis; used for fuzzy information granulation and feature extraction in Section 2.1. The forecast quality may depend strongly on this value.
Number of fuzzy clusters = 3
Set in FIC-MG (Table 6, Center of clustering 3); low/mean/high granules. No justification or sensitivity analysis.
Rough-set threshold r1 = 0.3
Set in Section 2.2 as the lower approximation threshold; the paper states r1 and r2 are set to 0.3 and 0.7 without experimental justification.
Rough-set threshold r2 = 0.7
Set in Section 2.2 as the upper approximation threshold; no sensitivity analysis.
Ensemble weights w1..w4 = Not reported
Fitted by IMOSFO on the validation set to minimize MAPE and MSE (Section 2.5 and 4.1). The fitted weight values are not presented.
IMOSFO hyperparameters = archive=100, dim=4, ub=2, lb=-2, pollination=0.1, mortality=0.1, grids=30
Set in Table 6; the paper argues archive 100 is optimal but provides no systematic tuning results.
Neural network hyperparameters = See Table 6
Learning rates, batch sizes, neuron counts, tree number are fixed; no tuning or sensitivity analysis.

assumptions (5)

domain assumption Triangular membership function with parameters Low, R, Up captures the relevant variation in each 36-point wind speed window
Invoked in Section 2.2 A; the choice of fuzzy granule determines the features used by all downstream predictors.
domain assumption Fuzzy C-means clustering with three centers extracts features that are useful for wind speed point and interval prediction
Used in Section 2.2 B; if the clustering features are not predictive, the whole FIC-MG module is ineffective.
standard math The four base learners are correctly implemented and their standard training procedures behave as expected
The paper relies on standard implementations of BiLSTM, CNN-GRU, LSTM-XGB, and RF without providing code.
domain assumption The training/validation/test split of 60/20/20 respects the temporal order and contains no leakage
Section 3.1 describes the split; the paper does not state how the FIC-MG windows are computed relative to this split.
standard math The DM test assumptions (finite variance of loss differences) hold for the wind speed error series
Used in Section 4.2 to claim significance.

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Cite this review

Pith. "Pith review of A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction." pith.science (2026). https://pith.science/paper/MKM6MIXL

@misc{pith2026250705761,
  author       = {Pith},
  title        = {Pith review of: A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MKM6MIXL}},
  note         = {Machine review of arXiv:2507.05761}
}

read the original abstract

Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This system employs a convex subset partitioning approach using a triangular affiliation function for fuzzy feature extraction. By applying soft clustering to the subsets, constructing an affiliation matrix, and identifying clustering centers, the system introduces the concepts of inner and boundary domains. It subsequently calculates the distances from data points to the clustering centers by measuring both interclass and intraclass distances. This method updates the cluster centers using the membership matrix, generating optimal feature values. Building on this foundation, we use multiple machine learning methods to input the fuzzy features into the prediction model and integrate learning techniques to predict feature values. Because different datasets require different modeling approaches, the integrated weight-updating module was used to dynamically adjust model weights by setting a dual objective function to ensure the accuracy and stability of the prediction. The effectiveness of the proposed model in terms of prediction performance and generalization ability is demonstrated through an empirical analysis of data from the Penglai wind farm.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

[1]

Introduction Recently, many countries have been grappling with energy shortage, which impedes global sustainable development. The sharp rise in global energy prices has posed a serious threat to industries in both Asian and European nations , with the potential for power outages becoming a significant concern . As the global energy crisis deepens, renewab...

work page 2021
[2]

However, this approach lacks a solid theoretical foundation , which can lead to incomplete noise removal or the unintentional elimination of relevant non-noise components

Most data preprocessing methods currently rely on VMD techniques to separate data into different frequency components, typically treating the highest frequencies as white noise to be 4 eliminated. However, this approach lacks a solid theoretical foundation , which can lead to incomplete noise removal or the unintentional elimination of relevant non-noise ...

work page
[3]

This is particularly true when appropriate optimization measures are lacking , which considerably limits the effectiveness and applicability of a single approach

Relying on a single forecasting method can compromise the stability and accuracy of predictions. This is particularly true when appropriate optimization measures are lacking , which considerably limits the effectiveness and applicability of a single approach

work page
[4]

An overly complex model may result in overfitting, where the model performs exceptionally well on the training dataset but fails to generalize effectively to the test dataset. To address the limitations of historical wind speed prediction models, this study proposes a novel hybrid wind speed prediction framework , CGT-BF, aimed at overcoming the challenge...

work page
[5]

By applying soft clustering to the subsets, the method constructs an affiliation matrix and clustering centers

We propose the FIC -MG method, an innovative data preprocessing approach that combines fuzzy information granulation and fuzzy rough C -means clustering. By applying soft clustering to the subsets, the method constructs an affiliation matrix and clustering centers. It introduces the concepts of inner and boundary domains, which when combined with intercla...

work page
[6]

This system comprises a point prediction framework and an interval prediction framework, which complement each other

We introduce a dual-framework system to address the issue of ignoring wind speed uncertainty single-use point wind speed predictions. This system comprises a point prediction framework and an interval prediction framework, which complement each other. Traditional wind speed prediction methods primarily focus on point prediction; however, the uncertainty o...

work page
[7]

This system uses information granulation to accurately predict chaotic time series

We develop a multivariate intelligent granular combination prediction system (CGT-BF) to address the challenge of chaotic time series prediction. This system uses information granulation to accurately predict chaotic time series. It expands the single variable of the time series into a multidimensional space and transform s it into a granular interval. Th...

work page
[8]

The IMOSFO algorithm optimizes point prediction and granularity in two phases

We propose the enhanced IMOSFO algorithm, which is proven to be effective in obtaining Pareto-optimal solutions from a theoretical perspective. The IMOSFO algorithm optimizes point prediction and granularity in two phases. In the first stage, IMOSFO conducts global search optimization via a mapping mechanism that initializes the spatial distribution of so...

work page

Show all 12 references

[9]

membership function

Experimental methods and data In this section, we provide a detailed description of the proposed prediction system CGT-BF. The system is composed of the FIC -MG fuzzy feature extraction method and the innovative ensemble learning method T-BF. T-BF integrates several core machi...

2023
[10]

The experiment involved a simulation analysis using wind speed measurement data from Penglai, Shandong

Results of the four experiments 3.1 Experiment Ⅰ In Experiment Ⅰ, we focused on the performance of the proposed method relative to those of traditional single machine learning methods for point and interval predictions and employed the proposed method for wind speed prediction...
[11]

Additionally, we highlighted the broad applicability of the model across various scenarios and validated the superiority of the improved optimization algorithm

Discussion Through validation in the four experiments, we clearly demonstrated the substantial advantages of the proposed method in both point and interval predictions. Additionally, we highlighted the broad applicability of the model across various scenarios and validated the...
[12]

Repeated Wavelet Transform Based Arima Model for Very Short-Term Wind Speed Forecasting

Conclusion Wind energy is inherently variable, making accurate wind speed forecasting crucial for wind farm operation and grid safety. However, many existing studies often overlook effective data preprocessing methods and adaptive forecasting strategies, leading to suboptimal ...

2019

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Introduction Recently, many countries have been grappling with energy shortage, which impedes global sustainable development. The sharp rise in global energy prices has posed a serious threat to industries in both Asian and European nations , with the potential for power outages becoming a significant concern . As the global energy crisis deepens, renewab...

work page 2021

[2] [2]

However, this approach lacks a solid theoretical foundation , which can lead to incomplete noise removal or the unintentional elimination of relevant non-noise components

Most data preprocessing methods currently rely on VMD techniques to separate data into different frequency components, typically treating the highest frequencies as white noise to be 4 eliminated. However, this approach lacks a solid theoretical foundation , which can lead to incomplete noise removal or the unintentional elimination of relevant non-noise ...

work page

[3] [3]

This is particularly true when appropriate optimization measures are lacking , which considerably limits the effectiveness and applicability of a single approach

Relying on a single forecasting method can compromise the stability and accuracy of predictions. This is particularly true when appropriate optimization measures are lacking , which considerably limits the effectiveness and applicability of a single approach

work page

[4] [4]

An overly complex model may result in overfitting, where the model performs exceptionally well on the training dataset but fails to generalize effectively to the test dataset. To address the limitations of historical wind speed prediction models, this study proposes a novel hybrid wind speed prediction framework , CGT-BF, aimed at overcoming the challenge...

work page

[5] [5]

By applying soft clustering to the subsets, the method constructs an affiliation matrix and clustering centers

We propose the FIC -MG method, an innovative data preprocessing approach that combines fuzzy information granulation and fuzzy rough C -means clustering. By applying soft clustering to the subsets, the method constructs an affiliation matrix and clustering centers. It introduces the concepts of inner and boundary domains, which when combined with intercla...

work page

[6] [6]

This system comprises a point prediction framework and an interval prediction framework, which complement each other

We introduce a dual-framework system to address the issue of ignoring wind speed uncertainty single-use point wind speed predictions. This system comprises a point prediction framework and an interval prediction framework, which complement each other. Traditional wind speed prediction methods primarily focus on point prediction; however, the uncertainty o...

work page

[7] [7]

This system uses information granulation to accurately predict chaotic time series

We develop a multivariate intelligent granular combination prediction system (CGT-BF) to address the challenge of chaotic time series prediction. This system uses information granulation to accurately predict chaotic time series. It expands the single variable of the time series into a multidimensional space and transform s it into a granular interval. Th...

work page

[8] [8]

The IMOSFO algorithm optimizes point prediction and granularity in two phases

We propose the enhanced IMOSFO algorithm, which is proven to be effective in obtaining Pareto-optimal solutions from a theoretical perspective. The IMOSFO algorithm optimizes point prediction and granularity in two phases. In the first stage, IMOSFO conducts global search optimization via a mapping mechanism that initializes the spatial distribution of so...

work page