Landslide data analysis using various time-series forecasting models

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

RAIRO - Operations Research ◽

10.1051/ro/2019117 ◽

2020 ◽

Vol 54 (2) ◽

pp. 597-614

Author(s):

Shanoli Samui Pal ◽

Samarjit Kar

Keyword(s):

Genetic Algorithm ◽

Time Series ◽

Linear Regression ◽

Regression Model ◽

Choquet Integral ◽

Time Series Forecasting ◽

Fuzzy Measure ◽

Model Parameters ◽

Forecasting Models ◽

Non Linear

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

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Time-Series Forecasting Models for Gasoline Prices in China

International Journal of Economics and Finance ◽

10.5539/ijef.v10n12p43 ◽

2018 ◽

Vol 10 (12) ◽

pp. 43

Author(s):

Feng Xu ◽

Mohamad Sepehri ◽

Jian Hua ◽

Sergey Ivanov ◽

Julius N. Anyu

Keyword(s):

Time Series ◽

Arima Model ◽

Time Series Forecasting ◽

Gasoline Prices ◽

Support Vector ◽

Grey System ◽

Gasoline Price ◽

Forecasting Models ◽

Long Run ◽

Environmental Protection Policies

Accurate prediction of gasoline price is important for the automobile makers to adjust designs and productions as well as marketing plans of their products. It is also necessary for government agencies to set effective inflation monitoring and environmental protection policies. To predict future levels of the gasoline price, due to difficulties of obtaining accurate estimates of influential external factors, data driven time-series forecasting models thus become more suitable given the convenience and practicability they are providing. In this paper, five popular time-series forecasting models, i.e., ARIMA-GARCH, exponential smoothing, grey system, neural network, and support vector machines models, are applied to predict gasoline prices in China. Comparing the performances of these models, it is noted that for this specific time series, a parsimonious ARIMA model performs the best in predicting the gasoline prices for a short time horizon, while for the medium length and long run the SVR and FNN models outperforms others respectively.  

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Research on financial time series forecasting model based on big data analysis

10.1117/12.2624566 ◽

2021 ◽

Author(s):

Xiaoyan Huang

Keyword(s):

Time Series ◽

Big Data ◽

Data Analysis ◽

Financial Time Series ◽

Big Data Analysis ◽

Time Series Forecasting ◽

Forecasting Model ◽

Financial Time ◽

Model Based

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Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026820500170 ◽

2020 ◽

Vol 19 (02) ◽

pp. 2050017

Author(s):

Nghiem Van Tinh

Keyword(s):

Time Series ◽

Historical Data ◽

Pso Algorithm ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Forecasting Accuracy ◽

Forecasting Models ◽

Proposed Model ◽

Fuzzy C Means Clustering ◽

Universe Of Discourse

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

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