A new method of time series forecasting using intuitionistic fuzzy set based on average-length

Abhishekh,; Gautam, Surendra Singh; Singh, Suryabhan

doi:10.1080/21681015.2020.1768163

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Recently, the authors in [13] studied the performance of bagging tree regression, random forest (RF) regression, and gradient boosting regression algorithms by predicting the sales demand of multiple products from different stores in the next three months; the results showed that the performance of fuzzy time series regression was the best. Reference [14] proposed a model for intuitionistic FTS forecasting based on the average length of the interval, which enhances the forecasting result.…”

Section: Fashion Sales Forecasting Methodsmentioning

confidence: 99%

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Tsao,

Liu,

et al. 2023

Fibres &Amp; Textiles in Eastern Europe

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This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent forecasting was proposed. In the first stage, a web crawler was used to browse a B2C apparel website to identify popular products. In the second stage, machine learning methods were used to predict the sales demand for new products. Additionally, we used Google Trends to collect external information indices to adjust the demand forecasting. Our numerical study shows that the intelligent forecasting approach can effectively reduce the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 45.79, 26.35, and 26.34 %, respectively.

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Section: Fashion Sales Forecasting Methodsmentioning

confidence: 99%

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Tsao,

Liu,

et al. 2023

Fibres &Amp; Textiles in Eastern Europe

View full text Add to dashboard Cite

show abstract

“…FTS models are transferred to IFTS models (e.g., Chen [27], Cheng et al [4], and Chang and Huang [32]). Besides, the results of one recent FTS [33] and one IFTS [17] approaches are also adopted for comparison in Table 5. The direction accuracy (DA) and RMSE obtained with these models of and the forecasts obtained with the proposed model are listed in Table 5.…”

Section: Figurementioning

confidence: 99%

“…After the work by Castillo et al [15], an increasing number of studies in recent years have extended FTS to IFTS [16][17][18][19][20]. However, most of these studies focused on improving prediction results and did not consider uncertainty or hesitation when facing forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method

Chen¹,

Chen²,

Chang³

et al. 2021

IJCIS

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Traditional time series methods can predict seasonal problems, but not problems with transferred linguistic data. Thus, a forecasting method for such problems is required. However, existing intuitionistic fuzzy time series forecasting methods lack persuasiveness in determining the degree of hesitation and the lengths of intervals. Hence, this research is mainly to explore how to decide the degree of hesitation for each interval for intuitionistic fuzzy time series. This paper proposes the weighted intuitionistic fuzzy time series model based on the Nth quantile discretization approach (NQDA). The proposed model can decide the appropriate number, interval length, degree of hesitation, and membership and nonmembership functions of linguistic values on the basis of the training data. In the experimental section, the forecasts of several data sets are made for model validation. Results indicate that the proposed model can be used to obtain forecasts for other time-related data sets.

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Second Order Intuitionistic Fuzzy Time Series Forecasting Model via Crispification

Alam

Ramli

2022

Lecture Notes in Networks and Systems

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A new method of time series forecasting using intuitionistic fuzzy set based on average-length

Cited by 7 publications

References 25 publications

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method

Second Order Intuitionistic Fuzzy Time Series Forecasting Model via Crispification

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