scholarly journals Noise Filtering Using FFT, Bayesian Model and Trend Model for Time Series Data.

1999 ◽  
Vol 5 (3) ◽  
pp. 113-127 ◽  
Author(s):  
Mitsue ONODERA ◽  
Yoshimi ISU ◽  
Umpei NAGASHIMA ◽  
Hiroaki YOSHIDA ◽  
Haruo HOSOYA ◽  
...  
Keyword(s):  
Time Series ◽  
Bayesian Model ◽  
Time Series Data ◽  
Series Data ◽  
Noise Filtering ◽  
Trend Model

scholarly journals Growth Model and Forecasting Prices of Some Agricultural Products in Bangladesh

2021 ◽  
Vol 26 (2) ◽  
pp. 64-71
Author(s):  
Md Hossain
Keyword(s):  
Time Series ◽  
Growth Model ◽  
Selection Criteria ◽  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Trend Model ◽  
Exponential Method

The aim of this paper was to explore the appropriate deterministic time series model using the latest selection criteria considering the price pattern of onion, garlic and potato products in Bangladesh (January 2000 to December 2016). It appeared from our analysis that the time series data for the prices of potato was first order homogenous stationary but onion and garlic were found to be the second order stationary. Four different forecasting models namely, linear trend model, quadratic trend model, exponential growth model, and S-curve trend model were used to find the best fitted model for the prices of above mentioned products in the Bangladesh. Three accuracy measures such as mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean squared deviation (MSD) were used for the selection of the best fitted model based on lowest value of forecasting error. Lowest values of these errors indicated a best fitted model. After choosing the best growth model by the latest model selection criteria, prices of selected agricultural commodities were forecasted using the following time-series analysis methods: Simple Exponential Method, Double Exponential Method using the time period from January 2017 to December 2021. The findings of this study would be useful for policy makers, researchers, businessmen as well as producers in order to forecast future prices of these commodities.

Export Trend of Ready Made Garments (RMG) Sector of Bangladesh

2021 ◽  
Vol 13 (1) ◽  
pp. 101-110
Author(s):  
M. S. Hossain ◽  
M. T. Uddin
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Gross Domestic Product ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Series Data ◽  
Upward Trend ◽  
Trend Model ◽  
Significant Upward Trend ◽  
The Government

Ready-Made Garments (RMG) sector has greater importance than any other sector in Bangladesh in terms of growth, employment, foreign exchange earnings and Gross Domestic Product (GDP). The objective of this study is to determine the export trend of the RMG sector in Bangladesh by using different trend models. To serve the objectives of the study, time-series data of RMG sectors has been used for the period 1985-2018. Among several trend models, the Semi-log Parabolic Trend model is found to be the best-fitted model for determining the trend of RMG exports. From the empirical results of the study, it is observed that RMG exports have a significant upward trend for the period 1985-2018 with a growth rate of 8.76 % in 2018. The forecasted RMG export will be nearly 31712.82 million USD in the financial year 2022-23. The findings of the study will help the government, NGO’s and policymakers to undertake necessary steps for the progress of this sector.

scholarly journals Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 9720
Author(s):  
Sungwon Kim ◽  
Meysam Alizamir ◽  
Nam Won Kim ◽  
Ozgur Kisi
Keyword(s):  
Time Series ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Time Series Data ◽  
Model Averaging ◽  
Uncertainty Estimation ◽  
Series Data ◽  
Streamflow Forecasting ◽  
Forecasting Accuracy ◽  
Daily Streamflow

Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.

scholarly journals Partisan Selectivity in Blame Attribution: Evidence from the COVID-19 Pandemic

2021 ◽  
Author(s):  
Matthew H. Graham ◽  
Shikhar Singh
Keyword(s):  
Time Series ◽  
Bayesian Model ◽  
Time Series Data ◽  
Motivated Reasoning ◽  
Series Data ◽  
Original Time Series ◽  
Blame Attribution ◽  
Survey Experiments ◽  
Original Survey ◽  
Original Time

Crises and disasters give voters an opportunity to observe the incumbent's response and reward or punish them for successes and failures. Yet even when voters agree on the facts, they tend to attribute responsibility in a group-serving manner, disproportionately crediting their party for positive developments and blaming opponents for negative developments. Using original time series data, we show that partisan disagreement over U.S. President Donald Trump's responsibility for the COVID-19 pandemic quickly emerged alongside the pandemic's onset in March 2020. Three original survey experiments show that the valence of information about the country's performance against the virus contributes causally to such gaps. A Bayesian model of information processing anticipates our findings more closely than do theories of partisan-motivated reasoning. These findings shed new light on the foundations of partisan loyalty, especially among citizens who do not think of themselves as partisans.

scholarly journals A hierarchical Bayesian model for flexible module discovery in three-way time-series data

2015 ◽  
Vol 31 (12) ◽  
pp. i17-i26 ◽  
Author(s):  
David Amar ◽  
Daniel Yekutieli ◽  
Adi Maron-Katz ◽  
Talma Hendler ◽  
Ron Shamir
Keyword(s):  
Time Series ◽  
Bayesian Model ◽  
Time Series Data ◽  
Series Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Module Discovery

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