A Novel Stochastic Fuzzy Time Series Forecasting Model Based on a New Partition Method

Abstract

Fuzzy Time Series (FTS) models are commonly used in time series forecasting, where they do not require any statistical assumptions on time series data. FTS models can handle data sets with a small number of observations or with uncertainty. This is a general advantage of FTS as compared with other techniques. However, FTS models still have some criticisms, such as the optimal lengths of intervals and the proper weights, which always influence the model accuracy and still have been of many concerns in literature. The work in this paper proposes a novel FTS forecasting model based on a new tree partitioning method (TPM) and Markov chain (MC), called FTSMC-TPM, for determining the optimal partitions of intervals and the proper weights vectors respectively, and this will greatly improve the model accuracy. The efficiency of the FTSMC-TPM model is tested using two types of time series consisting of the air pollution index (API) data, which is collected from Kuala Lumpur, Malaysia and the benchmark data of the University of Alabama enrollment. Three statistical criteria have been used for investigating the accuracy of the proposed model. The results indicate that the FTSMC-TPM model outperforms the existing classic and advanced time series models in terms of forecasting accuracy. In addition, the FTSMC-TPM model shows the ability to successfully deal with forecasting problems to obtain higher model accuracy, which is examined in comparison with the existing models to validate its superiority. Hence, this study demonstrates that the proposed model is more suitable for the accurate prediction of air pollution events as well as for forecasting any type of random time series.

A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

“…IoT tools are used to acquire real-time data on product location, condition, and environment. Numerous IoTbased logistic systems have been proposed in the literature [12][13][14][15][16][17]. In contrast, blockchain technology is a potential solution for improving supply chain transparency, security, and traceability; however, there is still a need for further research to fully understand its implications and to develop best practices for its implementation [7].…”

Downlink Massive MIMO Systems: Reduction of Pilot Contamination for Channel Estimation with Perfect Knowledge of Large-Scale Fading

“…However, the DR in massive MIMO systems is limited by PC resulting from the non-orthogonal PRS transmitted by users in the same cell and neighboring cells. The challenge of PC addressed and EC improved, we proposed a method that utilizes orthogonal PRS and considers the effects of LSF.To address the problem of PC, we assigned pilot PRS to specific user groups, which helped mitigate the issue as the number of antenna elements increased [29][30][31][32][33]. In addition to maximizing the SNR for each user equipment (UE) 𝐾𝐾, MRT precoding technique also addressed power constraints, which helped mitigate interference.…”

“…we strive to maximize achievable data rates for edge users. This approach has the potential to significantly improve connectivity and throughput at the network edge, contributing to advancements in wireless communication systems [29]. In order to obtain high-quality CE, the base station (BS) establishes a connection between the training signal and a predefined PRS specific to each user equipment (UE), leveraging comprehensive knowledge.…”

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

“…Time series models arrange data chronologically and leverage the presumed repetition of patterns from past periods into the present and future. The purpose of time series model analysis is to uncover patterns for modeling future events (Alyousifi et al, 2021 ), identifying a variety of patterns that show to be influential on the response variable (Wu et al, 2023 ), enhancing forecasting accuracy and stability from the perspectives of noise distribution and outliers (Yang et al, 2023 ), and exploring how a particular model selection can be better applied to forecast the new data in the future (Xu et al, 2023 ). Based on the abundant data and its diverse attributes consisting of trends, seasonality, and cyclicality, current values are often modeled based on past data exhibiting inter-variable correlations, commonly through linear or nonlinear models.…”