Long‐term intuitionistic fuzzy time series forecasting model based on vector quantisation and curve similarity measure

Fan, Xiaoshi; Lei, Yong; Wang, Yanan; Lu, Yanli

doi:10.1049/iet-spr.2015.0496

Cited by 16 publications

(7 citation statements)

References 40 publications

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“…Qualitative sales forecasting methods mainly include the subjective probability method, expert judgment opinion method, Delphi method, mutual influence method, scenario prediction method, etc [ 2 – 4 ]. Quantitative sales forecasting methods mainly include the time-series method (e.g., autoregressive series analysis [ 5 ], ARIMA model [ 6 ]), machine learning method [ 7 ] (e.g., artificial neural network [ 8 , 9 ], extreme learning machine [ 10 ], support vector machine (SVR) [ 11 ], and ensemble algorithms), and deep learning method.…”

Section: Related Work Of Demand Forecastingmentioning

confidence: 99%

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

Li¹,

Zhang²

2022

Computational Intelligence and Neuroscience

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Based on real sales data, this article constructed LGBM and LSTM sales prediction models to compare and verify the performance of the proposed models. In this article, we forecast the product sales of stores in the future T + 3 days and use MAPE as the evaluation index. The experiment shows that the product sales prediction model based on the convolutional LSTM (ConvLSTM) network has better prediction accuracy. From a store point of view, ConvLSTM prediction model MAPE was 0.42 lower than the long short-term memory (LSTM) network and 0.68 lower than LGBM. From the perspective of commodity categories, different commodity categories are suitable for different forecasting methods. Some categories are suitable for regression forecasting, while others are suitable for time-series forecasting. Among the categories suitable for time-series forecasting, the ConvLSTM model performs the best.

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Section: Related Work Of Demand Forecastingmentioning

confidence: 99%

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

Li¹,

Zhang²

2022

Computational Intelligence and Neuroscience

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“…The intuitionistic fuzzy c-means algorithm was utilized to introduce the modeling and implementation of intuitionistic FTS. Various Intuitionistic FTS approaches were proposed (Joshi et al 2016;Wang et al 2016;Fan et al 2016;Kumar and Gangwar 2016). Using triangle membership functions with equal and unequal intervals, Bisht and Kumar (2016) created hesitant FSs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid EMPSO-PFTS Method for Fuzzy Time Series Forecasting: Integrating Picture Fuzzy Sets and Exponentially Mutated Particle Swarm Optimization

Dutta,

Rath

2023

Preprint

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Current research has demonstrated that fuzzy sets can be used to address forecasting issues. Researchers have created numerous fuzzy time series (FTS) approaches without taking into account the non-determinacy. For a considerable period, researchers have consistently focused on two significant key issues: determining the optimal interval size and incorporating non-determinacy. The focus of this article is to present a groundbreaking picture fuzzy time series (PFTS) forecasting model that is constructed based on the principles of picture fuzzy sets (PFSs). A PFS represents a generalized form of fuzzy and intuitionistic fuzzy sets. Here picture fuzzy clustering (PFC) technique is utilized for the construction of PFS. In this article, we integrate PFS and exponentially mutated particle swarm optimization (EMPSO) to develop a novel hybrid EMPSO-PFTS forecasting method. Optimal length is determined by EMPSO, and non-determinacy is taken into account by PFS when time series data is fuzzy. The suggested forecasting method is used on data sets from the University of Alabama and the market price of the State Bank of India (SBI-P) at the Bombay Stock Exchange, India, to demonstrate its applicability and usefulness. Mean square error (MSE) and average forecasting error (AFE) are used to gauge the effectiveness of the proposed method. The significant reduction in both MSE and AFE is strong evidence of the superior performance of the proposed EMPSO-PFTS method compared to various existing methods. To ensure the reliability and validity of the proposed method, rigorous statistical validation and performance analysis are conducted.

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“…Kumar and Gangwar [50] determined intuitionistic fuzzy relations to solve the intuitionistic fuzzy time series. Fan et al [51] proposed a long term intuitionistic fuzzy time series forecasting model. Egrioglu et al [52] introduced a new intuitionistic fuzzy time series forecasting method based on pi-sigma artificial neural networks and artificial bee colony.…”

Section: Introductionmentioning

confidence: 99%

A new explainable robust high-order intuitionistic fuzzy time-series method

2021

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Fuzzy time series forecasting methods based on type-1 fuzzy sets continue to have largely proposed in the literature. These methods use only membership values in determining fuzzy relations. However, Intuitionistic fuzzy time series models basically use both membership values and nonmembership values. So, it can be considered that the using of intuitionistic fuzzy time forecasting models will be able to increase the forecasting performance in the fuzzy time series analyses because of the fact that more information is used. Therefore, Intuitionistic fuzzy time series models have started to use for solving the real-life series in the fuzzy time series literature since 2013. In this study, a new explainable robust high order intuitionistic fuzzy time series forecasting method are proposed based on new defined model. In the proposed method, the algorithm of intuitionistic fuzzy c-means is used for fuzzification of observations and a robust regression method is used to determine fuzzy relations. Because the robust regression is employed to define fuzzy relation, all inputs of the method can be explainable and they can be statistically tested and commented. Applications of this study have been made by using energy data of Primary Energy Consumption (PEC) between 1965 and 2016 for 23 countries in the region of Europe&Eurasia. Forecasting performances obtained from these applications by using the proposed method have been compared with performances of some other time series method in the literature and the results have been discussed.

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Long‐term intuitionistic fuzzy time series forecasting model based on vector quantisation and curve similarity measure

Cited by 16 publications

References 40 publications

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

A Novel Hybrid EMPSO-PFTS Method for Fuzzy Time Series Forecasting: Integrating Picture Fuzzy Sets and Exponentially Mutated Particle Swarm Optimization

A new explainable robust high-order intuitionistic fuzzy time-series method

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