A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

Zhang, Yanpeng; Qu, Hua; Wang, Weipeng; Zhao, Jihong

doi:10.1155/2020/9546792

Cited by 18 publications

(15 citation statements)

References 44 publications

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“…The obtained results show that the DT2FTW model achieves stateof-the-art performance for global benchmarks for uncertain time-series. These results show a significant difference between the DT2FTW model and its counterpart DTW model, clustering-DTW model [15], and the fuzzy deep artificial neural network (ANN) model [16]. The results, shown in Table 3, confirm that the proposed DT2FTW model performs better, with lower error rates in terms of the RMSE, MAE, and MPE metrics, for all scenarios.…”

Section: Comparative Study and Experimental Resultsmentioning

confidence: 67%

“…The DTW approach is currently used in many areas, including online signature matching and handwriting recognition [9], gestures and sign language recognition [10], knowledge discovery, data mining and clustering [11], pattern recognition and data analysis [12], and signal processing [13,14]. In addition, in many real-world applications temporal problems are complex, uncertain, and chaotic [15]. Fuzzy logic is one of the most effective methods for handling uncertainties in dynamic and nonstationary environments [16].…”

Section: Literature Reviewmentioning

confidence: 99%

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Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Safari¹,

Hosseini²,

Mazinani³

2021

IJFIS

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Prediction of time series is associated with nondeterministic pattern analysis for uncertain conditions. Therefore, it is necessary to develop high-quality prediction methods for real-world applications. Type-2 fuzzy systems can handle high-order uncertainties, such as sequential dependencies associated with time series. Precise and reliable prediction can help to develop reasonable strategies and assist specialists in planning the best policies for modeling events in uncertain time series. In this study, a hybrid model (dynamic type-2 fuzzy time warping [DT2FTW]) was proposed for handling high-order uncertainties in time-series prediction. A type-2 fuzzy intelligent system was developed alongside a dynamic time warping algorithm for predicting the patterns' similarity in long-time series for time-series prediction. The results demonstrate that the proposed DT2FTW model yields more reliable predictions on global standard benchmarks such as the Mackey-Glass, Dow Jones, and NASDAQ time-series. The results also confirm that the proposed DT2FTW model has lower error rates than its counterpart algorithms in terms of the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MPE). In addition, the results confirm the superiority of the proposed model with an average area under the ROC curve (AUC) of 94%, with the 95% confidence interval (92%-95%).

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Section: Comparative Study and Experimental Resultsmentioning

confidence: 67%

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Safari¹,

Hosseini²,

Mazinani³

2021

IJFIS

View full text Add to dashboard Cite

show abstract

“…Zhang et al. [45] developed FTS forecasting model based on time series clustering and multiple linear regressions. While the FCM clustering technique uses Euclidean distance (ED) which gets easily stuck in a noisy environment and doesn’t obtain good results.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India

Kumar

2022

ISA Transactions

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World is facing stress due to unpredicted pandemic of novel COVID-19. Daily growing magnitude of confirmed cases of COVID-19 put the whole world humanity at high risk and it has made a pressure on health professionals to get rid of it as soon as possible. So, it becomes necessary to predict the number of upcoming cases in future for the preparation of future plan-of-action and medical set-ups. The present manuscript proposed a hybrid fuzzy time series model for the prediction of upcoming COVID-19 infected cases and deaths in India by using modified fuzzy C-means clustering technique. Proposed model has two phases. In phase-I, modified fuzzy C-means clustering technique is used to form basic intervals with the help of clusters centroid while in phase-II, these intervals are upgraded to form sub-intervals. The proposed model is tested against available COVID-19 data for the measurement of its performance based on mean square error, root mean square error and average forecasting error rate. The novelty of the proposed model lies in the prediction of COVID-19 infected cases and deaths for next coming 31 days. Beside of this, estimation for the approximate number of isolation beds and ICU required has been carried out. The projection of the present model is to provide a base for the decision makers for making protection plan during COVID-19 pandemic.

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“…Then, all the segments were projected into the same dimensional space, according to the coefficients of the model. Zhang [9] proposed a fuzzy time-series forecasting model based on multiple linear regression and time-series clustering for forecasting market prices.…”

Section: Introductionmentioning

confidence: 99%

GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement

Wang

2021

Algorithms

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Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results.

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A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

Cited by 18 publications

References 44 publications

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India

GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement

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