Engineering Proceedings, Volume 5, 2021

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolutio...

The current pandemic situation of SARS-Cov-2 is negatively influencing people worldwide, and leading to high mortality and excess mortality, due to more reasons than only the disease itself. Thus, forecasting of the mortality rates and consequent pop...

Simulation-based forecast model selection considers two candidate forecast model classes, simulates from both models fitted to data, applies both forecast models to simulated structures, and evaluates the relative benefit of each candidate prediction...

Real-world time series data often contain missing values due to human error, irregular sampling, or unforeseen equipment failure. The ability of a computational interpolation method to repair such data greatly depends on the characteristics of the ti...

This paper addresses the problem of the unsupervised approach of credit card fraud detection in unbalanced datasets using the ARIMA model. The ARIMA model is fitted to the regular spending behaviour of the customer and is used to detect fraud if some...

The Matrix Profile (MP) algorithm has the potential to revolutionise many areas of data analysis. In this article, several applications to financial time series are examined. Several approaches for the identification of similar behaviour patterns (or...

As the world’s largest exporter and second-largest importer, China has made exchange rate stability a top priority for its economic growth. With development over decades, however, China now holds excess dollar reserves that have suffered a huge paper...

Scrub typhus, an infectious disease caused by a bacterium transmitted by “chigger” mites, constitutes a public health problem in Thailand. Predicting epidemic peaks would allow implementing preventive measures locally. This study analyses the predict...

In the age of AI, companies strive to extract benefits from data. In the first steps of data analysis, an arduous dilemma scientists have to cope with is the definition of the ’right’ quantity of data needed for a certain task. In particular, when de...

Epidemiology maths resorts to Susceptible-Infected-Recovered (SIR)-like models to describe contagion evolution curves for diseases such as Covid-19. Other time series estimation approaches can be used to fit and forecast curves. We use data from the...

Estimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to men...

On the Island of Vulcano (Aeolian Archipelago, Italy) the temperatures of fumarole emissions, have ranged from about 700 °C to the boiling point. Since the end of the last eruption (1890 A.D.), many periods of increased heating of hydrothermal system...

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling...

We present a new forecasting scheme based on the credibility distribution of fuzzy events. This approach allows us to build prediction intervals using the first differences of the time series data. Additionally, the credibility expected value enables...

Forecasting often involves multiple time-series that are hierarchically organized (e.g., sales by geography). In that case, there is a constraint that the bottom level forecasts add-up to the aggregated ones. Common approaches use traditional forecas...

Recently, there has been growing interest in using machine learning based methods for forecasting renewable energy generation using time-series prediction. Such forecasting is important in order to optimize energy management systems in future micro-g...

In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the...

The contribution of this paper is two-fold. First, we present ProbCast—a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a fram...

This paper aims at comparing different forecasting strategies combined with the STL decomposition method. STL is a versatile and robust time series decomposition method. The forecasting strategies we consider are as follows: three statistical methods...

In this paper, the scope is to study whether and how the COVID-19 situation affected the unemployment rate in Greece. To achieve this, a vector autoregression (VAR) model is employed and data analysis is carried out. Another interesting question is w...

This work deals with the Traffic State Estimation (TSE) problem for urban networks, using heterogeneous sources of data such as stationary flow sensors, Floating Car Data (FCD), and Automatic Vehicle Identifiers (AVI). A data-based flow and density e...

Wind power forecasting is a tool used in the energy industry for a wide range of applications, such as energy trading and the operation of the grid. A set of models known as decomposition-based hybrid models have stood out in recent times due to prom...

In time series analyses, covariance modeling is an essential part of stochastic methods such as prediction or filtering. For practical use, general families of covariance functions with large flexibilities are necessary to model complex correlations...

The availability of massive amounts of temporal data opens new perspectives of knowledge extraction and automated decision making for companies and practitioners. However, learning forecasting models from data requires a knowledgeable data science or...

Picking an appropriate parameter setting (meta-parameters) for visualization and embedding techniques is a tedious task. However, especially when studying the latent representation generated by an autoencoder for unsupervised data analysis, it is als...

The COVID-19 pandemic has left a mark on nearly all events since the start of the year 2020. There are many studies that examine the medical, economic, and social effects of the pandemic; however, only a few are concerned with how the reactions of so...

At present, different methods are used for processing GPS time series data obtained from a network of GNSS stations. Solutions converted to velocity and displacement allow the generation of different geodynamic models in areas influenced by tectonic...

The output gap, the difference between potential and actual output, has a direct impact on policy decisions, e.g., monetary policy. Estimating this gap and its further analysis remain the subject of controversial debates due to methodological problem...

In this paper, we discuss the statistical coherence between financial time series in terms of Rényi’s information measure or entropy. In particular, we tackle the issue of the directional information flow between bivariate time series in terms of Rén...

Two-sample and independence tests with the kernel-based mmd and hsic have shown remarkable results on i.i.d. data and stationary random processes. However, these statistics are not directly applicable to nonstationary random processes, a prevalent fo...

This paper presents an analysis of the long-term dynamics of the terms of trade of primary commodities (TTPC) using an extended data set for the whole period 1900–2020. Following our original contribution, we implement three approaches of time series...

Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting mo...

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 me...

The increasing integration of renewable energy sources into the existing energy supply structure is challenging due to the intermittency typical of these energy sources, which implies problems of reliability and scheduling of grid operation. Concerni...

A new Big Data cluster method was developed to forecast the hotel accommodation market. The simulation and training of time series data are from January 2008 to December 2019 for the Spanish case. Applying the Hierarchical and Sequential Clustering A...

GNSS geodetic time series analysis allows the study of the geodynamic behavior of a specific terrestrial area. These time series define the temporal evolution of the geocentric or topocentric coordinates obtained from geodetic stations, which are lin...

Window operation is among one of the most influential factors on indoor air quality (IAQ). In this paper, we focus on the modeling of the windows’ opening state in a real open-plan office with five windows. The IAQ of this open-plan office was monito...

In this paper, we want to find a continuous function fitting through the discrete covariance sequence generated by a stationary AR process. This function can be determined as soon as the Yule–Walker equations are found. The procedure consists of two...

This paper presents a systematic review of Python packages with a focus on time series analysis. The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an overview of the...

Many geodetic measurement data can be modelled as a multivariate time series consisting of a deterministic (“functional”) model describing the trend, and a stochastic model of the correlated noise. These data are also often affected by outliers and t...

In this paper, we use the Logarithmic Mean Divisia Index (LMDI) to apply decomposition analysis on Carbon Dioxide (CO₂) emissions from transport systems in seven Eastern European countries over the period between 2005 and 2015. The results show that...

GNSS systems allow precise resolution of the geodetic positioning problem through advanced techniques of GNSS observation processing (PPP or relative positioning). Current instrumentation and communications capabilities allow obtaining geocentric and...

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions...

The publication is devoted to studying asymptotic properties of statistical estimates of the distribution parameters $u\in R^q$ of a multidimensional random stationary time series $z_t\in R^m,t\in \mathbb{Z}$ satisfying the strong mixing conditions. We consider estimat...

This paper investigates the research question of whether the principle of parsimony carries over into interval forecasting, and proposes new semiparametric prediction intervals that apply the block bootstrap to the first-order autoregression. The AR(...

Electric load forecasting is becoming increasingly challenging due to the growing penetration of decentralised energy generation and power-electronics based loads such as heat pumps and electric vehicles, which adds to a transition to more variable w...

In this paper, we describe a machine learning approach for predicting machine health indicators with a large time horizon into the future. The approach uses state-of-the-art neural network architectures for sequence modelling and can incorporate nume...

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence ($\mathcal{E}SJS$) and the Kolmogorov–Smirnov two-sample test statistic ($KS2$), are...

Periodic series of period T can be mapped into the set of permutations of $[T-1]=\{1,2,3,\dots ,T-1\}$. These permutations of period T can be classified according to the relative ordering of their elements by the horizontal visibility map. We prove that the n...

This study compares the effectiveness of COVID-19 control policies on the virus’s spread and on the change of the infection dynamics in China, Germany, Austria, and the USA relying on a regression discontinuity in time and ‘earlyR’ epidemic models. T...