Advances in Time Series Analysis and Forecasting with Applications in Disaster and Climate Risk Management

Dear Colleagues,

Advancements in time series analysis have improved precision and accuracy in the analysis of non-Gaussian, nonlinear, and non-stationary systems over time. These methods provide valuable insights for applications such as forecasting financial risks and socioeconomic time series.

The growing interest in solutions for improving disaster risk management, climate adaptation, and resilience necessitates the development of advanced models to assess the risks induced by climatic/meteorological trends or multi-hazard disasters. Such assessments and forecasts are essential to building effective solutions for managing these risks as well as increasing adaptation and resilience. Recently, advanced machine learning models have been developed for weather and climate forecasting, and they are used in climate adaptation, early warning systems, and disaster risk reduction. However, they need further development to be employed in practical  settings, from disaster and climate impact forecasting models to scenario-based risk assessments, while taking into account the non-stationary, complex, and high-dimensional nature of these risk factors.

This Special Issue focuses on the latest advancements in time series analysis and forecasting methods and their applications in disaster risk mitigation and climate adaptation. This includes (but is not limited to) advanced methods for time series analysis (e.g., nonlinear/nonparametric time series, deep learning forecasting models, forecasting risk measures), their applications in disaster risk management and forecasting climatic risks, and their impact on socioeconomic variables. By assembling research on both theoretical and practical time series analysis methods for risk assessment, this Special Issue aims to highlight innovative approaches and their practical applications for addressing challenges in building resilience to disasters and climate risk.

Dr. Mohammad Reza Yeganegi
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• nonlinear time series
• deep learning in time series analysis and forecasting
• forecasting distribution
• impact of climate on financial/economic time series
• impact of climate on energy risks
• disaster-induced financial shocks
• climatic/meteorological hazard forecasting
• drought and heatwave forecasting
• flood forecasting
• early warning