Time-series data optimized AR/ARMA model for frugal spectrum estimation in Cognitive Radio

2021 ◽  
Vol 44 ◽  
pp. 101252
Author(s):  
Debashis Chakraborty ◽  
Salil Kr. Sanyal
Keyword(s):  
Time Series ◽  
Cognitive Radio ◽  
Time Series Data ◽  
Arma Model ◽  
Series Data ◽  
Spectrum Estimation

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

scholarly journals Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1576
Author(s):  
Wanhyun Cho ◽  
Sangkyuoon Kim ◽  
Myunghwan Na ◽  
Inseop Na
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Environmental Factors ◽  
Environmental Variables ◽  
Time Series Data ◽  
Arma Model ◽  
Series Data ◽  
Proposed Model ◽  
Lstm Network ◽  
Statistical Time

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

scholarly journals Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wei Lili ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Time Series Data ◽  
Arma Model ◽  
Railway Track ◽  
Series Data ◽  
Railway Transportation ◽  
Track Geometry ◽  
Track Irregularity ◽  
Railway Passenger

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

scholarly journals An ARMA Model for Short-term Prediction of Hepatitis B Virus Seropositivity among Blood Donors in Lafia-Nigeria

2019 ◽  
pp. 1-11
Author(s):  
David Adugh Kuhe ◽  
Thomas Akwana Obed
Keyword(s):  
Time Series ◽  
Hepatitis B Virus ◽  
Virus Infection ◽  
Hepatitis B ◽  
Blood Donors ◽  
Time Series Data ◽  
Arma Model ◽  
Series Data ◽  
Hepatitis B Virus Infection ◽  
B Virus

In this paper, we attempt to search for an optimal Autoregressive Moving Average (ARMA) model that best forecast hepatitis B virus infection among blood donors in Lafia-Nigeria. The study uses monthly data in Lafia-Nigeria for the period of 11 years 6 months from January 2007 to June 2018. The data was obtained as secondary data from General Hospital Lafia and Dalhatu Araf Specialist Hospital, Lafia. The time series and stationarity properties of the data are explored using time plots and Dickey-Fuller Generalized Least Squares unit root test. The results indicate that the series is integrated of order zero, I(0). An ARMA (p,q) model in line with Box-Jenkins procedure was employed to model the time series data. The result shows that ARMA (1,1) was the best candidate to model and forecast hepatitis B virus infection among blood donors in Lafia- Nigeria. Critical analysis of the model shows that the HBV infection is chronic among blood donors in the study area. The estimated ARMA (1,1) model was then used to forecast future values of hepatitis B infection among blood donors in Lafia-Nigeria from July 2018 to June 2019. The forecast shows a stable level of infection for the forecasted period. The study provided some policy recommendations.

scholarly journals Autocovariance and autocorrelation structures of the generalised autoregressive moving average (GARMA(1,3;δ,1)) model

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Suguneswary Ellappan ◽  
Norhashidah Awang ◽  
Thulasyammal Ramiah Pillai
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arma Model ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Numerical Examples ◽  
New Class ◽  
Autocorrelation Structure ◽  
Special Case

Generalized ARMA (GARMA) model is a new class of model that has been introduced to reveal some unknown features of certain time series data. The objective of this paper is to derive the autocovariance and autocorrelation structure of GARMA(1,3;δ,1)  model in order to study the behaviour of the model. It is shown that the results of this model can be reduced to the autocovariance and autocorrelation of the standard ARMA model as well as a special case. Numerical examples are used to illustrate the behaviour of the autocovariance and autocorrelation at different δ values to show the various structures that the model can represent

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
Sign in / Sign up

Export Citation Format

Share Document