scholarly journals Model and Variable Selection Procedures for Semiparametric Time Series Regression

2009 ◽  
Vol 2009 ◽  
pp. 1-37 ◽  
Author(s):  
Risa Kato ◽  
Takayuki Shiohama
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Variable Selection ◽  
Time Series Data ◽  
Selection Procedure ◽  
Semiparametric Regression ◽  
Information Criteria ◽  
Series Data ◽  
Unknown Parameters ◽  
Time Series Regression

Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

scholarly journals Model selection in the reconstruction of regulatory networks from time-series data

2009 ◽  
Vol 2 (1) ◽  
pp. 68
Author(s):  
Eugene Novikov ◽  
Emmanuel Barillot
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Series Data

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

scholarly journals Bristlecone: An F# library for model-fitting and model-selection of ecological time-series data

2019 ◽  
Author(s):  
Andrew C Martin
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Tree Rings ◽  
Time Series Data ◽  
Sediment Cores ◽  
Model Fitting ◽  
Series Data ◽  
Ecological Problems ◽  
Biodiversity And Ecosystem Function

Environmental archives such as sediment cores and tree rings provide important insights on the timing and rates of change in biodiversity and ecosystem function over the long-term. Such datasets are often analysed using empirical methods, which limits their ability to address ecological questions that seek to understand underlying ecological mechanisms and processes. Top down modelling approaches – where data is confronted with simple ecological models – can be used to infer the presence, form, and strength of mechanisms of interest. To aid adoption of time-series mechanistic modelling for long-term ecology, we created a F# library, Bristlecone, that can be used to apply this approach using a Model- Fitting and Model-Selection workflow. Our objective with Bristlecone was to create a library that could be used to efficiency and effectively conduct a full MFMS analysis for long-term ecological problems. We incorporated techniques to address specific challenges with environmental archives, including uneven time steps from age-depth models (for sediment cores), and allometry and seasonality (for tree rings). We include an example analysis to demonstrate functionality of Bristlecone. Our solution presents a straightforward, repeatable, and highly parallel method for conducting inference for long- term ecological problems.

On the Autoregressive Time Series Model Using Real and Complex Analysis

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 716-728
Author(s):  
Torsten Ullrich
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Closed Form ◽  
Autoregressive Model ◽  
Complex Analysis ◽  
Time Series Data ◽  
Selection Process ◽  
Global Structure ◽  
Series Data ◽  
Time Step

The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process.

BAYESIAN 3-DIMENSIONAL SPATIAL VARIABLE SELECTION MODELING OF VOXEL-SPECIFIC HRFs FOR LOCALIZATION IN fMRI TIME SERIES DATA

2018 ◽  
Vol 51 (6) ◽  
pp. 397-426
Author(s):  
Nasrin Borumandnia ◽  
Hamid Alavi Majd ◽  
Farid Zayeri ◽  
Ahmad Reza Baghestani ◽  
Mahmood Reza Gohari ◽  
...  
Keyword(s):  
Time Series ◽  
Variable Selection ◽  
Time Series Data ◽  
Spatial Variable ◽  
Series Data ◽  
3 Dimensional ◽  
Fmri Time Series

Two-step variable selection in partially linear additive models with time series data

2018 ◽  
Vol 47 (3) ◽  
pp. 661-671 ◽  
Author(s):  
Mu Feng ◽  
Zhao Chen ◽  
Ximing Cheng
Keyword(s):  
Time Series ◽  
Variable Selection ◽  
Time Series Data ◽  
Additive Models ◽  
Series Data ◽  
Partially Linear

scholarly journals Peramalan Data Runtun Waktu menggunakan Model Hybrid Time Series Regression – Autoregressive Integrated Moving Average

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Melisa Arumsari ◽  
◽  
Andrea Dani ◽  
Keyword(s):  
Time Series ◽  
Hybrid Method ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Regression ◽  
Accurate Forecast ◽  
Using Data ◽  
The Individual ◽  
Time Series Data Analysis

Forecasting is a method used to estimate or predict a value in the future using data from the past. With the development of methods in time series data analysis, a hybrid method was developed in which a combination of several models was carried out in order to produce a more accurate forecast. The purpose of this study was to determine whether the TSR-ARIMA hybrid method has a better level of accuracy than the individual TSR method so that more accurate forecasting results are obtained. The data in this study are monthly data on the number of passengers on American airlines for the period January 1949 to December 1960. Based on the analysis, the TSR-ARIMA hybrid method produces a MAPE of 3,061% and the TSR method produces an MAPE of 7,902%.

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