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The Future of Bitcoin: a Synchrosqueezing Wavelet Transform to Predict Search Engine Query Trends
2016
International Workshop on Knowledge Discovery on the Web
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We are going to test it using datasets extracted from search engine trends, using a cloud of keywords related to the Bitcoin topic. ...
Particularly, the Synchrosqueezing Wavelet Transform -SST allows signal decomposition and instantaneous frequency extrusion, at the same time promising consistent reconstruction capabilities, hence the ...
We are going to study a prediction system based on Google Trends in order to test the validity of such hypothesis. ...
dblp:conf/kdweb/StocchiLIBM16
fatcat:gnjyrwos3vhulnfrrqrxtydt6e
An Approach on MCSA-Based Fault Detection Using Discrete Wavelet Transform and Fault Classification Based on Deep Neural Networks
2021
International Journal of Advanced Trends in Computer Science and Engineering
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Fast Fourier Transform (FFT) converts signals from time domain to frequency domain on the other hand Discrete Wavelet Transform (DWT) gives complete three-dimensional information of the signal, frequency ...
Discrete Wavelet Transform is preferred over the Fast Fourier Transform (FFT). ...
Figure 1 : 1 Propose Methodology of the Research An Approach on MCSA-Based Fault Detection Using Discrete Wavelet Transform and Fault Classification Based on Deep Neural Networks Many fault detection methods ...
doi:10.30534/ijatcse/2021/1081032021
fatcat:yx3rpvfeeba6labuo4w4gsu35y
A review on the applications of wavelet transform for streamflow and suspended sediment analysis
2017
Tehnički Vjesnik
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; (ii) multi-scale trend analysis; (iii) prediction and forecasting of series with wavelet based hybrid black-box models, and (iv) wavelet-aided simulation of synthetic series. ...
Overview of mentioned wavelet transform advantages is given in this paper with focus on discharge and suspended sediment time series, presented through: (i) multi-temporal scale analysis of series variability ...
On the other hand, topics dealing with multi-temporal scale analysis of series variability along with prediction and forecasting of series with wavelet based hybrid black-box models are in greater number ...
doi:10.17559/tv-20160613095312
fatcat:fzhs5at645frpkf7nuyydelkji
Detection of outbreaks from time series data using wavelet transform
2003
AMIA Annual Symposium Proceedings
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In this paper, we developed a new approach to detection of disease outbreaks based on wavelet transform. ...
It is capable of dealing with two problems found in real-world time series data, namely, negative singularity and long-term trends, which may degrade the performance of current approaches to outbreak detection ...
WAD, on the other hand, only performs one wavelet transform to remove the long-term trend and then applies a simple detection algorithm to the residual data once. ...
pmid:14728273
pmcid:PMC1479935
fatcat:mas4mzxqxvev7nqzlb7zk3m6wq
A Method for the Monthly Electricity Demand Forecasting in Colombia based on Wavelet Analysis and a Nonlinear Autoregressive Model
2011
Ingeniería
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A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and ...
The method preprocesses the time series using a Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT); a study for the selection of the mother wavelet and her order, as well as the level ...
We propose a pre-processing of the original time series to identify and separate the trend, this stage will use a multi-resolution analysis (MRA) based on discrete wavelet transform (DWT) in order to find ...
doaj:46b800675d9c46248be426ed70f11cbe
fatcat:b7qdjylidbdlhf22ykntyvot4u
Virtualized Resource Allocation Algorithm in Mobile Internet
2016
International Journal of Grid and Distributed Computing
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However, due to the diversification of its businesses, small service scheduling granularity and flexibility, for this feature, we propose resource allocation prediction method by SVM and wavelet decomposition ...
Chaired provincial above topic 3 items, editor of three textbooks. ...
Based on previous research and analysis on system resources, establish specific forecast model can be achieved effectively predict within the next point time resource usage. ...
doi:10.14257/ijgdc.2016.9.6.20
fatcat:3wp6u4h56zg75ow242boosf2oi
Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things
2020
Sensors
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The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. ...
The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments. ...
Wavelet-based filtration is based on the fact that some approximation and detail coefficients from the wavelet transformation represent the signal noise, other than the signal trend. ...
doi:10.3390/s20030620
fatcat:xmurdjsppzd4ro4xq6ubfskiyi
Electricity Consumption Prediction Using XGBoost Based on Discrete Wavelet Transform
2017
DEStech Transactions on Computer Science and Engineering
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In this paper, we propose a hybrid model which integrate discrete wavelet transform and XGBoost to forecast the electricity consumption time series data on the long-term prediction, namely DWT-XGBoost. ...
Although few articles mentions the topic of electricity consumption prediction, numerous papers include some topic similar to the topic in this paper, such as rainfall forecasting, wind speed prediction ...
Features Extraction Based On Wavelet Transform In some previous studies, researchers have used wavelet transform to extract features. ...
doi:10.12783/dtcse/aiea2017/15003
fatcat:qelkh55iv5gjhgnvr2sqdiamrq
System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model
2018
International Journal of Electrical and Computer Engineering (IJECE)
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This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). ...
The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal ...
Based on
for the WFFNN model. ...
doi:10.11591/ijece.v8i4.pp2327-2337
fatcat:bxda2rsrg5ehlayz3hhya3qo2u
A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling
2013
2013 IEEE Conference on Prognostics and Health Management (PHM)
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based on cumulative function; • Analyze fitness of features in terms of monotonicity and trendability. ...
A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling. • Extract new features by applying trigonometric functions; • Build new features ...
The next topic is dedicated to application of WT, that is discrete wavelet transform (DWT) in particular. 1) Discrete wavelet transform: In signal processing, WT is considered as an efficient approach ...
doi:10.1109/icphm.2013.6621413
fatcat:44mshwag6rd4jiwof6elozib7m
Case study: shipping trend estimation and prediction via multiscale variance stabilisation
2016
Journal of Applied Statistics
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Empirical percentage coverage of 95% prediction intervals based on transformed ets forecasting based on eight different forecasting horizons, h. h
3
6
9
1 2
1 5
1 8
2 1
2 4
Box-Cox
0.86
1.21 ...
Possibly, one could rewrite the software, so that it makes use of a wavelet transform for non-dyadic data. This is both time consuming, pernickety and not adopted here. ...
doi:10.1080/02664763.2016.1260096
fatcat:k2h6jsqyrjdu5ejxqbxrc5nspm
Forecasting of Busy Telephone Traffic Based on Wavelet Transform and ARIMA-LSSVM
2014
International Journal of Smart Home
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factors based on wavelet transform and ARIMA-LSSVM. ...
From the simulation experiment results based on the actual telephone traffic data, it concludes that the combined model has higher prediction accuracy and strong generalization ability. ...
Conclusion We propose a combined traffic forecasting model considering the influence of multiple factors based on wavelet transform and ARIMA-LSSVM. ...
doi:10.14257/ijsh.2014.8.4.11
fatcat:k3qvodjdkjb37iq2vo54mnucbm
Review: Denoising and Compression Methods
2018
International Journal of Trend in Scientific Research and Development
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Oriented Wavelet Transform for Image Compression and Denoising Vivien Chappelier and Christine Guillemot, In this paper, a new transform for image processing, based on wavelets and the lifting paradigm ...
Maleki, In this paper, wavelet based image coding the choice of wavelets is crucial and determines the coding International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 ...
CONCLUSION In this paper we have taken a biomedical image for de-noising and compression in Wavelet Toolbox specially Wavelet 2D in MATLAB and MATLAB command prompt using step by step. ...
doi:10.31142/ijtsrd11521
fatcat:7ylh5bmq6nhohnb2bttj7jgefu
An improved wavelet–ARIMA approach for forecasting metal prices
2014
Resources policy
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The approach demonstrated in this paper is novel because it identifies the optimal combination of the wavelet transform type, wavelet function and the number of decomposition levels used in the MRA and ...
The performance of ARIMA models in forecasting metal prices is demonstrated to be increased substantially through a wavelet-based multiresolution analysis (MRA) prior to ARIMA model fitting. ...
Already, the wavelet-ARIMA technique enables the prediction of short-term trends and metal price movements over a forecast horizon of less than one year. ...
doi:10.1016/j.resourpol.2013.10.005
fatcat:53bsuaj2yrfbbgtffuvmwb2qxq
Prophet model and Gaussian process regression based user traffic prediction in wireless networks
2020
Science China Information Sciences
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User traffic prediction is an important topic for wireless network operators. A user traffic prediction method based on Prophet and Gaussian process regression is proposed in this paper. ...
The proposed method first employs discrete wavelet transform to decompose the user traffic time series to high-frequency component and low-frequency component. ...
Data decomposition based on discrete wavelet transform For user i, one-level wavelet decomposition is applied to per-user traffic time series x i (t) = x i , obtaining scaling coefficients c i (n) and ...
doi:10.1007/s11432-019-2695-6
fatcat:dkwmgn7u5nhpzmdliq4rwu2cqe
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