scholarly journals Gaussian processes for time-series modelling

2013 ◽  
Vol 371 (1984) ◽  
pp. 20110550 ◽  
Author(s):  
S. Roberts ◽  
M. Osborne ◽  
M. Ebden ◽  
S. Reece ◽  
N. Gibson ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Processes ◽  
Domain Knowledge ◽  
Time Series Data ◽  
Process Models ◽  
Series Data ◽  
Parametric Modelling ◽  
Case Examples ◽  
Time Series Modelling ◽  
Time Series Data Analysis

In this paper, we offer a gentle introduction to Gaussian processes for time-series data analysis. The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes . We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the approaches.

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

Examining the Long Run Effects of Export, Import and FDI Inflows on the FDI Outflows from India: A Causality Analysis

2009 ◽  
Vol 10 (1) ◽  
pp. 65-88
Author(s):  
Nandita Dasgupta
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Driving Force ◽  
Time Series Data ◽  
Series Data ◽  
Causality Analysis ◽  
Fdi Inflows ◽  
Long Run ◽  
Time Series Data Analysis ◽  
Trade And Investment

The objective of this paper is to examine the effects of international trade and investment related macro economic variables, namely, exports, imports and FDI inflows on the outflows of FDI from India over 1970 through 2005. Using time series data analysis, the empirical part of the paper finds unidirectional Granger Causality from export and import to FDI outflows but no such causality exists from FDI inflows to the corresponding outflows from India. Results confirm the assumption that lagged imports and exports are a driving force of ing front.

scholarly journals Periodic Time Series Data Analysis by Deep Learning Methodology

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223078-223088
Author(s):  
Haolong Zhang ◽  
Haoye Lu ◽  
Amiya Nayak
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Periodic Time Series ◽  
Time Series Data Analysis

Democratic peace: Does ethnic inclusiveness reduce interstate conflict?

2019 ◽  
pp. 019251211988473
Author(s):  
Seung-Whan Choi ◽  
Henry Noll
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Estimation Method ◽  
Interstate Conflict ◽  
Model Specification ◽  
Series Data ◽  
Democratic Peace ◽  
Interstate Disputes ◽  
Time Series Data Analysis ◽  
Cross National

In this study, we argue that ethnic inclusiveness is an important democratic norm that fosters interstate peace. When two states are socialized into the notion of ethnic tolerance, they acquire the ability to reach cooperative arrangements in time of crisis. Based on cross-national time-series data analysis covering the period 1950–2001, we illustrate how two states that are inclusive of their politically relevant ethnic groups are less likely to experience interstate disputes than states that remain exclusive. This finding was robust, regardless of sample size, intensity of the dispute, model specification, or estimation method. Therefore, we believe in the existence of ethnic peace: ethnic inclusiveness represents an unambiguous force for democratic peace.

Robust Extrema Features for Time-Series Data Analysis

2013 ◽  
Vol 35 (6) ◽  
pp. 1464-1479 ◽  
Author(s):  
Pramod K. Vemulapalli ◽  
Vishal Monga ◽  
Sean N. Brennan
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Data Analysis
Sign in / Sign up

Export Citation Format

Share Document