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Stats
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Time Series Analysis and Forecasting
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Time Series Analysis and Forecasting

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 10074

Special Issue Editors

Dr. Marco André da Silva Costa

E-Mail Website
Guest Editor
Águeda School of Technology and Management (ESTGA) & Center for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3754-909 Águeda, Portugal
Interests: state-space models; Kalman filtering; linear models; time series analysis; environmental statistics; data analysis; distribution-free estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Magda Sofia Valério Monteiro

E-Mail Website
Guest Editor
Águeda School of Technology and Management (ESTGA) & Center for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3754-909 Águeda, Portugal
Interests: time series analysis; statistical modeling; state-space models; statistical inference; data analysis; count data; environmental statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the impact of time series analysis and forecast on scientific applications has been increasing. The areas of application are very diverse, ranging from economics to biology and social sciences.

This Special Issue will focus on relevant contributions on the time series analysis and forecast methods in order to promote the rapid dissemination of novel research ideas and statistical applications on this field.

Contributions submitted to this Special Issue must be original work representing new time series modeling and forecast approaches or relevant applications based on real data set from different areas, such as environmental sciences, management, economics, and biological or social sciences.

Special Issue topics include (but are not limited to):

  • Nonparametric and functional methods
  • State space models
  • Linear and nonlinear models
  • Outliers in time series data
  • Models for count time series
  • Econometric models
  • Artificial neural networks and machine learning
  • Forecasting theory and adjustment
  • Forecasting in real problems
  • Forecasting performance evaluation
  • Interval forecasting
  • Change-point detection for time series

Dr. Marco André da Silva Costa
Dr. Magda Sofia Valério Monteiro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Stats is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 491 KiB  
Article
Model Free Inference on Multivariate Time Series with Conditional Correlations
by Dimitrios Thomakos, Johannes Klepsch and Dimitris N. Politis
Stats 2020, 3(4), 484-509; https://doi.org/10.3390/stats3040031 - 3 Nov 2020
Cited by 1 | Viewed by 3154
Abstract
New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in a multivariate setting as well. We [...] Read more.
New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in a multivariate setting as well. We present exact results on the use of univariate transformations and on their combination for joint modeling of the conditional correlations: we show how the NoVaS transformed series can be combined and the likelihood function of the product can be expressed explicitly, thus allowing for optimization and correlation modeling. While this keeps the original “model-free” spirit of NoVaS it also makes the new multivariate NoVaS approach for correlations “semi-parametric”, which is why we introduce an alternative using cross validation. We also present a number of auxiliary results regarding the empirical implementation of NoVaS based on different criteria for distributional matching. We illustrate our findings using simulated and real-world data, and evaluate our methodology in the context of portfolio management. Full article
(This article belongs to the Special Issue Time Series Analysis and Forecasting)
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46 pages, 14106 KiB  
Article
Recovering Yield Curves from Dynamic Term Structure Models with Time-Varying Factors
by Hiroyuki Kawakatsu
Stats 2020, 3(3), 284-329; https://doi.org/10.3390/stats3030020 - 22 Aug 2020
Cited by 1 | Viewed by 2800
Abstract
A dynamic version of the Nelson-Siegel-Svensson term structure model with time-varying factors is considered for predicting out-of-sample maturity yields. Simple linear interpolation cannot be applied to recover yields at the very short- and long- end of the term structure where data are often [...] Read more.
A dynamic version of the Nelson-Siegel-Svensson term structure model with time-varying factors is considered for predicting out-of-sample maturity yields. Simple linear interpolation cannot be applied to recover yields at the very short- and long- end of the term structure where data are often missing. This motivates the use of dynamic parametric term structure models that exploit both time series and cross-sectional variation in yield data to predict missing data at the extreme ends of the term structure. Although the dynamic Nelson–Siegel–Svensson model is weakly identified when the two decay factors become close to each other, their predictions may be more accurate than those from more restricted models depending on data and maturity. Full article
(This article belongs to the Special Issue Time Series Analysis and Forecasting)
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21 pages, 4758 KiB  
Article
Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks
by Paulo Canas Rodrigues, Olushina Olawale Awe, Jonatha Sousa Pimentel and Rahim Mahmoudvand
Stats 2020, 3(2), 137-157; https://doi.org/10.3390/stats3020012 - 1 Jun 2020
Cited by 22 | Viewed by 3867
Abstract
A proper understanding and analysis of suitable models involved in forecasting currency exchange rates dynamics is essential to provide reliable information about the economy. This paper deals with model fit and model forecasting of eight time series of historical data about currency exchange [...] Read more.
A proper understanding and analysis of suitable models involved in forecasting currency exchange rates dynamics is essential to provide reliable information about the economy. This paper deals with model fit and model forecasting of eight time series of historical data about currency exchange rate considering the United States dollar as reference. The time series techniques: classical autoregressive integrated moving average model, the non-parametric univariate and multivariate singular spectrum analysis (SSA), artificial neural network (ANN) algorithms, and a recent prominent hybrid method that combines SSA and ANN, are considered and their performance compared in terms of model fit and model forecasting. Moreover, specific methodological and computational adaptations were conducted to allow for these analyses and comparisons. Full article
(This article belongs to the Special Issue Time Series Analysis and Forecasting)
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