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Modern Time Series Analysis II
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Modern Time Series Analysis II

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 7422

Special Issue Editors

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
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

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a Special Issue on “Modern Time Series Analysis”.

This Special Issue will focus on recent advances in the field of time series analysis and forecasting.

This issue welcomes original contributions concerning the modern time series analysis including modeling and forecasting, advances in high-dimensional multivariate modeling, advances in online learning time series, big data analysis, and also forecasting in real problems.

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

  • Distribution-free methods for time series
  • Outliers in time series data
  • Non-linear models for time series
  • Models for extremes in time series
  • High-dimensional multivariate modeling
  • Models for count time series
  • Econometric models
  • Artificial neural networks and machine learning
  • Time series analysis with computational intelligence
  • Change-point detection for time series
  • Multivariate time series models
  • Forecasting from complex/big data
  • Forecasting in real problems

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

Keywords

  • time-series analysis
  • distribution-free estimation
  • outliers in time series
  • count data
  • state-space models
  • big data time series
  • extreme time series
  • forecasting

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Related Special Issues

Published Papers (4 papers)

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Research

19 pages, 2811 KiB  
Article
Prepivoted Augmented Dickey-Fuller Test with Bootstrap-Assisted Lag Length Selection
by Somak Maitra and Dimitris N. Politis
Stats 2024, 7(4), 1226-1243; https://doi.org/10.3390/stats7040072 - 17 Oct 2024
Viewed by 1176
Abstract
We investigate the application of prepivoting in conjunction with lag length selection to correct the size and power performance of the Augmented Dickey-Fuller test for a unit root. The bootstrap methodology used to perform the prepivoting is a residual based AR bootstrap that [...] Read more.
We investigate the application of prepivoting in conjunction with lag length selection to correct the size and power performance of the Augmented Dickey-Fuller test for a unit root. The bootstrap methodology used to perform the prepivoting is a residual based AR bootstrap that ensures that bootstrap replicate time series are created under the null irrespective of whether the originally observed series obeys the null hypothesis or not. Simulation studies wherein we examine the performance of our proposed method are given; we evaluate our method’s performance on ARMA(1,1) models with varying configurations for size and power performance. We also propose a novel data dependent lag selection technique that uses bootstrap data under the null to select an optimal lag length; the performance of our method is compared to existing lag length selection criteria. Full article
(This article belongs to the Special Issue Modern Time Series Analysis II)
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15 pages, 1718 KiB  
Article
The Negative Binomial INAR(1) Process under Different Thinning Processes: Can We Separate between the Different Models?
by Dimitris Karlis, Naushad Mamode Khan and Yuvraj Sunecher
Stats 2024, 7(3), 793-807; https://doi.org/10.3390/stats7030048 - 27 Jul 2024
Viewed by 1166
Abstract
The literature on discrete valued time series is expanding very fast. Very often we see new models with very similar properties to the existing ones. A natural question that arises is whether the multitude of models with very similar properties can really have [...] Read more.
The literature on discrete valued time series is expanding very fast. Very often we see new models with very similar properties to the existing ones. A natural question that arises is whether the multitude of models with very similar properties can really have a practical purpose or if they mostly present theoretical interest. In the present paper, we consider four models that have negative binomial marginal distributions and are autoregressive in order 1 behavior, but they have a very different generating mechanism. Then we try to answer the question whether we can distinguish between them with real data. Extensive simulations show that while the differences are small, we still can discriminate between the models with relatively moderate sample sizes. However, the mean forecasts are expected to be almost identical for all models. Full article
(This article belongs to the Special Issue Modern Time Series Analysis II)
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24 pages, 713 KiB  
Article
Hierarchical Time Series Forecasting of Fire Spots in Brazil: A Comprehensive Approach
by Ana Caroline Pinheiro and Paulo Canas Rodrigues
Stats 2024, 7(3), 647-670; https://doi.org/10.3390/stats7030039 - 27 Jun 2024
Cited by 1 | Viewed by 1189
Abstract
This study compares reconciliation techniques and base forecast methods to forecast a hierarchical time series of the number of fire spots in Brazil between 2011 and 2022. A three-level hierarchical time series was considered, comprising fire spots in Brazil, disaggregated by biome, and [...] Read more.
This study compares reconciliation techniques and base forecast methods to forecast a hierarchical time series of the number of fire spots in Brazil between 2011 and 2022. A three-level hierarchical time series was considered, comprising fire spots in Brazil, disaggregated by biome, and further disaggregated by the municipality. The autoregressive integrated moving average (ARIMA), the exponential smoothing (ETS), and the Prophet models were tested for baseline forecasts, and nine reconciliation approaches, including top-down, bottom-up, middle-out, and optimal combination methods, were considered to ensure coherence in the forecasts. Due to the need for transformation to ensure positive forecasts, two data transformations were considered: the logarithm of the number of fire spots plus one and the square root of the number of fire spots plus 0.5. To assess forecast accuracy, the data were split into training data for estimating model parameters and test data for evaluating forecast accuracy. The results show that the ARIMA model with the logarithmic transformation provides overall better forecast accuracy. The BU, MinT(s), and WLS(v) yielded the best results among the reconciliation techniques. Full article
(This article belongs to the Special Issue Modern Time Series Analysis II)
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17 pages, 3203 KiB  
Article
Climate Change: Linear and Nonlinear Causality Analysis
by Jiecheng Song and Merry Ma
Stats 2023, 6(2), 626-642; https://doi.org/10.3390/stats6020040 - 15 May 2023
Cited by 2 | Viewed by 3358
Abstract
The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. [...] Read more.
The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data. Full article
(This article belongs to the Special Issue Modern Time Series Analysis II)
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