A Novel Moving Average Forecasting Approach Using Fuzzy Time Series Data Set

Gautam, Surendra Singh; Abhishekh,

doi:10.1007/s40313-019-00467-w

Cited by 23 publications

(16 citation statements)

References 44 publications

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“…It has become a hot topic of research. The traditional prediction methods require prior knowledge of the data, such as exponential smoothing [ 17 ], moving average (MA) [ 18 ], auto-regression (AR) [ 19 ], auto-regressive integrated moving average (ARIMA) [ 20 ], etc. In practical systems, the traditional prediction methods cannot obtain a high accuracy prediction result due to the system’s complexity.…”

Section: Related Workmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.

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Section: Related Workmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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“…And every time if new data are available, parameters of these models have to be trained again to ensure the high accuracy level. As for some fuzzy time series based forecasting methods, the establishment of fuzzy sets is a little complicated and subjective [28]. Therefore, it is challenging to build a forecasting model that has a good forecasting accuracy with relatively low time complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Next, considering the node degree and time distance, the weights of different revised prediction values are assigned to obtain the final weighted result. To examine the prediction performance of this method, we conduct different experiments based on three time series, including the Construction cost index (CCI) [26], the student enrollment of the University of Alabama [50], [51], and the market price of State Bank of India Share [28]. The prediction results under different conditions show that this method is more accurate in terms of some error measurements and less time-consuming than many compared methods.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Zhao

Mo²,

Deng

2020

IEEE Access

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Recently time series prediction based on network analysis has become a hot research topic. However, how to more accurately forecast time series with good efficiency is still an open question. To address this issue, we propose an efficient time series forecasting method based on the DC algorithm and visibility relations on the vertexes set. Firstly, the time series is mapped into the network by the DC algorithm, which is a more efficient approach to generate the visibility graph. Then, we use the variation trends (slope) of those nodes that have visibility relation with the last node to get the preliminary predictive values. Afterward, the value of the last node is adopted to obtain the revised predictive values, which are assigned different weights according to node degree and time distance to get the final weighted result. To better demonstrate the prediction performance and applicability of the proposed method, the proposed method is applied to different time series data sets. The empirical results show that the proposed method could provide a higher level of forecasting accuracy than many methods with relatively lower time complexity. INDEX TERMS Complex network, time series forecasting, node degree, visibility graph, variation trend.

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“…Moving average (MA) is often used to model data in various fields of life. Studies on its application is seen in several literatures, such as [1], [2], [3], [4], and [5]. In fact, Reghunath et al in [1] used MA to analyze water resource data.…”

Section: Introductionmentioning

confidence: 99%

“…An automated trading system was developed based on moving averages from previous prices. Meanwhile, Gautam and Abhishekh in [5] used the model to analyze the data of fuzzy time series by reducing fluctuations.…”

Section: Introductionmentioning

confidence: 99%

Determining the Order of a Moving Average Model of Time Series Using Reversible Jump MCMC: A Comparison between Laplacian and Gaussian Noises

Suparman¹,

Salhi²,

Rusiman³

2020

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Moving average (MA) is a time series model often used for pattern forecasting and recognition. It contains a noise that is often assumed to have a Gaussian distribution. However, in various applications, noise often does not have this distribution. This paper suggests using Laplacian noise in the MA model, instead. The comparison of Gaussian and Laplacian noises was also investigated to ascertain the right noise for the model. Moreover, the Bayesian method was used to estimate the parameters, such as the order and coefficient of the model, as well as noise variance. The posterior distribution has a complex form because the parameters are concerened with the combination of spaces of different dimensions. Therefore, to overcome this problem, the Markov Chain Monte Carlo (MCMC) reversible jump algorithm is adopted. A simulation study was conducted to evaluate its performance. After it has worked properly, it was applied to model human heart rate data. The results showed that the MCMC algorithm can estimate the parameters of the MA model. This was developed using Laplace distributed noise. Moreover, when compared with the Gaussian, the Laplacian noise resulted in a higher order model and produced a smaller variance.

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A Novel Moving Average Forecasting Approach Using Fuzzy Time Series Data Set

Cited by 23 publications

References 44 publications

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Determining the Order of a Moving Average Model of Time Series Using Reversible Jump MCMC: A Comparison between Laplacian and Gaussian Noises

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