scholarly journals Time Series Seasonal Analysis Based on Fuzzy Transforms

2017 ◽  
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Time Series ◽  
Seasonal Forecasting ◽  
Weather Station ◽  
Fuzzy Transform ◽  
Italian Region ◽  
Mean Temperature ◽  
Forecasting Method ◽  
Seasonal Analysis ◽  
The Universe ◽  
Universe Of Discourse

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of a an assigned output. Like first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the following outputs: mean temperature, max temperature and min temperature, all considered daily. Like second example, we use the daily mean temperature measured at the weather station “Chiavari Caperana” in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, ARIMA and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples.

scholarly journals Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting

2019 ◽  
Vol 20 (1) ◽  
pp. 53
Author(s):  
Lutvia Citra Ramadhani ◽  
Dian Anggraeni ◽  
Ahmad Kamsyakawuni
Keyword(s):  
Time Series ◽  
Inflation Rate ◽  
Moving Average ◽  
Fuzzy Time Series ◽  
Accuracy Rate ◽  
Autoregressive Integrated Moving Average ◽  
Diagnostic Checking ◽  
Forecasting Method ◽  
The Universe ◽  
Universe Of Discourse

Saxena-Easo Fuzzy Time Series (FTS) is a softcomputing method for forecasting using fuzzy concept. It doesn’t need any assumption like conventional forecasting method. Generally it’s focused on three important steps like percentage change as the universe of discourse, interval partition, and defuzzification. In this research, this method is applied to Indonesia’s inflation rate data. The aim of this research is to forecast Indonesia’s inflation rate in 2017 by using input from Autoregressive Integrated Moving Average (ARIMA) process, Saxena-Easo FTS, and actual data from 1970-2016. ARIMA is focused on four steps like identifying, parameter estimation, diagnostic checking, and forecasting. The result for Indonesia’s inflation rate forecasting in 2017 is about 5.9182 using Saxena-Easo FTS. Root Mean Square Error (RMSE) is also computed to compare the accuracy rate from each method between Saxena-Easo FTS and ARIMA. RMSE from Saxena-Easo FTS is about 0.9743 while ARIMA is about 6.3046. Keywords: saxena-easo fuzzy time series, ARIMA, inflation rate, RMSE.

scholarly journals Seasonal Time Series Forecasting by F1-Fuzzy Transform

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3611 ◽  
Author(s):  
Di Martino ◽  
Sessa
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Prediction Method ◽  
Heat Index ◽  
Weather Data ◽  
Fuzzy Transform ◽  
Campania Region ◽  
Seasonal Time Series ◽  
Forecasting Method ◽  
Daily Weather Data

We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.

scholarly journals Application of fuzzy time series forecasting approach for predicting an enterprise net income level

2021 ◽  
Vol 280 ◽  
pp. 02007
Author(s):  
Kateryna Gorbatiuk ◽  
Pavlo Hryhoruk ◽  
Oksana Proskurovych ◽  
Nina Rizun ◽  
Audrius Gargasas ◽  
...  
Keyword(s):  
Time Series ◽  
Income Level ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Net Income ◽  
Accuracy Rate ◽  
The Sustainable Development ◽  
Point Forecast ◽  
The Universe ◽  
Universe Of Discourse

To ensure the sustainable development of an enterprise, it is necessary to properly analyze the enterprise development, to ground the plans and management decisions on effective diagnostics and prediction of current and future economic situation at the enterprise. The article presents a study on the application of fuzzy time series forecasting methods. A new approach is applied to forecasting an enterprise's net income using a fuzzy technique. For testing the methodology, there were used statistical data on the enterprise net income level of the Ukrainian enterprise from 2002 to 2017. In the method of Stevenson and Potter, it is proposed to use as the universe of discourse, in the process of applying the method for all defined fuzzy sets, the intervals of variation of such indicator as growth rate. The same background as in Stevenson and Porter’s model is used in this article for forecasting the time series levels using the growth rates of the actual data as the universe of discourse. The forecasting results, obtained by this approach, are supposed to have more accuracy rate than other fuzzy time series models. Some modifications of this technique are proposed to obtain a higher accuracy rate and a point forecast one step forward.

HEURISTIC BIVARIATE FORECASTING MODEL OF MULTI-ATTRIBUTE FUZZY TIME SERIES BASED ON FUZZY CLUSTERING

2012 ◽  
Vol 11 (01) ◽  
pp. 167-195 ◽  
Author(s):  
GUOFANG NAN ◽  
SHUAIYIN ZHOU ◽  
JISONG KOU ◽  
MINQIANG LI
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Bivariate Model ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Universe ◽  
Universe Of Discourse

Fuzzy time series has been applied to forecast various domain problems because of its capability to deal with vagueness and incompleteness inherent in data. However, most existing fuzzy time series models cannot cope with multi-attribute time series and remain too subjective in the partition of the universe of discourse. Moreover, these models do not consider the trend factor and the corresponding external time series, which are highly relevant to target series. In the current paper, a heuristic bivariate model is proposed to improve forecasting accuracy, and the proposed model applies fuzzy c-means clustering algorithm to process multi-attribute fuzzy time series and to partition the universe of discourse. Meanwhile, the trend predictors are extracted in the training phase and utilized to select the order of fuzzy relations in the testing phase. Finally, the proper full use of the external series to assist forecasting is discussed. The performance of the proposed model is tested using actual time series including the enrollments at the University of Alabama, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and a sensor dataset. The experimental results show that the proposed model can be utilized for multi-attribute time series and significantly improves the average MAER to 1.19% when compared with other forecasting models.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Mortality Risk from Respiratory Diseases due to Non-Optimal Temperature among Brazilian Elderlies

2021 ◽  
Vol 18 (11) ◽  
pp. 5550
Author(s):  
Ludmilla da Silva Viana Jacobson ◽  
Beatriz Fátima Alves de Oliveira ◽  
Rochelle Schneider ◽  
Antonio Gasparrini ◽  
Sandra de Souza Hacon
Keyword(s):  
Time Series ◽  
Respiratory Diseases ◽  
Respiratory Health ◽  
The Elderly ◽  
Optimal Temperature ◽  
Average Temperature ◽  
Exposure Response ◽  
Mean Temperature ◽  
Risk Of Mortality ◽  
Exposure Variable

Over the past decade, Brazil has experienced and continues to be impacted by extreme climate events. This study aims to evaluate the association between daily average temperature and mortality from respiratory disease among Brazilian elderlies. A daily time-series study between 2000 and 2017 in 27 Brazilian cities was conducted. Data outcomes were daily counts of deaths due to respiratory diseases in the elderly aged 60 or more. The exposure variable was the daily mean temperature from Copernicus ERA5-Land reanalysis. The association was estimated from a two-stage time series analysis method. We also calculated deaths attributable to heat and cold. The pooled exposure–response curve presented a J-shaped format. The exposure to extreme heat increased the risk of mortality by 27% (95% CI: 15–39%), while the exposure to extreme cold increased the risk of mortality by 16% (95% CI: 8–24%). The heterogeneity between cities was explained by city-specific mean temperature and temperature range. The fractions of deaths attributable to cold and heat were 4.7% (95% CI: 2.94–6.17%) and 2.8% (95% CI: 1.45–3.95%), respectively. Our results show a significant impact of non-optimal temperature on the respiratory health of elderlies living in Brazil. It may support proactive action implementation in cities that have critical temperature variations.

Are “All-and-Some” Statements Falsifiable After All?: The Example of Utility Theory

1986 ◽  
Vol 2 (2) ◽  
pp. 185-195 ◽  
Author(s):  
Philippe Mongin
Keyword(s):  
Melting Point ◽  
Utility Theory ◽  
Logical Form ◽  
Empirical Science ◽  
Demarcation Criterion ◽  
Existential Sentences ◽  
The Universe ◽  
Universe Of Discourse

Popper's well-known demarcation criterion has often been understood to distinguish statements of empirical science according to their logical form. Implicit in this interpretation of Popper's philosophy is the belief that when the universe of discourse of the empirical scientist is infinite, empirical universal sentences are falsifiable but not verifiable, whereas the converse holds for existential sentences. A remarkable elaboration of this belief is to be found in Watkins's early work (1957, 1958) on the statements he calls “all-and-some,” such as: “For every metal there is a melting point.” All-and-some statements (hereafter AS) are both universally and existentially quantified in that order. Watkins argued that AS should be regarded as both nonfalsifiable and nonverifiable, for they partake in the logical fate of both universal and existential statements. This claim is subject to the proviso that the bound variables are “uncircumscribed” (in Watkins's words); i.e., that the universe of discourse is infinite.

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