## 4 Advancements in Industry 4.0 and Pandemic Management
### Time Series Analysis and Anomaly Detection  
The field of time series analysis [77] and anomaly detection [15; 35; 78; 79; 80; 81; 82] has gained significant attention in recent years, especially in the context of Industry 4.0. This review focuses on a collection of scholarly papers that present innovative methodologies, algorithms, and techniques aimed at efficiently monitoring, predicting [31; 83; 84], and detecting anomalies in time series data from various industrial applications. [PERSON] et al. [85] conducted a study on predicting the occurrence of the Fourth Industrial Revolution using time series analysis. They established an industrial revolution time prediction function based on quadratic functions and a time prediction model based on the gray GM (1,1) model. By analyzing the occurrence time sequences of the previous three industrial revolutions, the authors forecast that the fourth industrial revolution is likely to take place around 2055. This forthcoming revolution is expected to be characterized by technological advancements such as the Internet of things, big data, cloud computing, and intelligent manufacturing. Authors of this review [86] propose a predictive model for system degradation using Bayesian time series models. The authors emphasize the importance of efficient maintenance in industrial equipment, and they address the limitations of deterministic prediction models that fail to consider the uncertainty inherent in degradation measurements. To overcome this, they introduce the use of time series models obtained through the Facebook Prophet algorithm, allowing for the prediction of degradation evolution in turbomachinery. The model is validated using data from large-scale industrial centrifugal compressors, and it demonstrates promising results with well-covered confidence intervals. [PERSON] et al. [87] focused on improving the quality of life and ecosystem services in smart cities through time series forecasting. They recognized that enhancing the perception of quality of life requires action on the green infrastructure of cities. The researchers utilized state-of-the-art methodologies, including IoT technology, big data, and artificial intelligence, to collect environmental data and performed time series prediction processes using generalized additive models (GAM). Unlike LSTM and ARIMA models, GAM proved to be more efficient in handling seasonality issues in the data. The work enabled informed decision making by local authorities regarding the implementation of urban actions, ensuring a positive impact on citizens and the environment. [PERSON] et al. [88] contribute to the field with an adaptive window rolling median methodology for time series anomaly detection. Given the rise of Industry 4.0 and the massive amounts of data collected from sensors, anomaly detection has become a crucial task. Existing anomaly detection methods are diverse due to the variety of anomalies encountered, and there is no universal approach. To address this challenge, the authors propose a novel anomaly detection algorithm that utilizes a rolling median with an adaptive sliding window. The window adapts based on two methods, F1-based and T-test, which aim to ensure an upward trend in F1 score and recognize trends in time series, respectively. The proposed algorithm is evaluated on benchmark data sets and real industrial machinery sensor observations, showing superior performance compared to an ensemble of seven existing models.