Analysis and prediction of time series using fuzzy transform

2008 ◽  
Author(s):  
Irina Perfilieva ◽  
Vilem Novak ◽  
Viktor Pavliska ◽  
Antonin Dvorak ◽  
Martin Stepnicka
Keyword(s):  
Time Series ◽  
Fuzzy Transform

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 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 Bitcoin Analysis and Forecasting Through Fuzzy Transform

2020 ◽  
Author(s):  
Maria Letizia Guerra ◽  
Laerte Sorini ◽  
Luciano Stefanini
Keyword(s):  
Time Series ◽  
Sentiment Analysis ◽  
Google Trends ◽  
Fuzzy Transform ◽  
Stylized Facts

Sentiment analysis to characterize properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy transform to capture stylized facts and mutual connections between time series having different nature. Six years of daily Bitcoin Prices and Google Trends are analyzed in order to establish new perspectives in the management of their dynamics.

scholarly journals Bitcoin Analysis and Forecasting through Fuzzy Transform

Axioms ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 139
Author(s):  
Maria Letizia Guerra ◽  
Laerte Sorini ◽  
Luciano Stefanini
Keyword(s):  
Time Series ◽  
Sentiment Analysis ◽  
Test Statistics ◽  
Local Smoothing ◽  
Fuzzy Transform ◽  
Short Term ◽  
Stylized Facts ◽  
Lp Norm ◽  
Daily Data ◽  
Short Term Forecasting

Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.

scholarly journals An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays

2021 ◽  
Vol 7 ◽  
pp. e409
Author(s):  
Faramarz Saghi ◽  
Mustafa Jahangoshai Rezaee
Keyword(s):  
Time Series ◽  
Natural Gas ◽  
Time Delays ◽  
Wavelet Decomposition ◽  
Vital Role ◽  
Optimal Time ◽  
Price Forecasting ◽  
Fuzzy Transform ◽  
Economic Sectors ◽  
Gas Price

Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. The purpose of this study is to provide an approach to forecast the natural gas price. First, optimal time delays are identified by a new approach based on the Euclidean Distance between input and target vectors. Then, wavelet decomposition has been implemented to reduce noise. Moreover, fuzzy transform with different membership functions has been used for modeling uncertainty in time series. The wavelet decomposition and fuzzy transform have been integrated into the preprocessing stage. An ensemble method is used for integrating the outputs of various neural networks. The results depict that the proposed preprocessing methods used in this paper cause to improve the accuracy of natural gas price forecasting and consider uncertainty in time series.

scholarly journals On the Similarity and Dependence of Time Series

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 550
Author(s):  
Vilém Novák ◽  
Soheyla Mirshahi
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Random Noise ◽  
Similarity Index ◽  
Index Properties ◽  
Fuzzy Transform ◽  
Financial Time ◽  
Selection Of

In this paper, we undertake the problem of evaluating interrelation among time series. Interrelation is measured using a similarity index. In this paper, we suggest a new one based on the known fuzzy transform (F-transform), which has been proven to remove higher frequencies than a given threshold and reduce the random noise significantly. The F-transform also provides an estimation of the slope of time series in a given imprecisely delineated time. We prove some of the suggested index properties and show its ability to measure similarity (and thus the interrelation) on a selection of several real financial time series. The method is well interpretable and easy to adjust.

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