## 1 Introduction  
In recent years, sensor technologies have been significantly developed to help generate large data at a relatively low cost [1]. The fields of the Internet of Things (IoT) [2], precision medicine [3], and industry 4.0 [4] produce advanced, large temporally annotated data. The analysis of this large temporal data, as it is, is a dilemma for researchers and data scientists. Thus, it encourages the reduction of large time-series data into smaller series and captures ample characteristics of primary data to improve the analysis. The resulting time series is the basis of machine learning applications, such as analysis of heartbeat [5] and respiratory rate [6], identification of high-risk patients who are at increased risk of infection [7], optimization of a production line [8], or incident detection over cloud [9]. The reduction of large data to feature-based representation is crucial, as the implementation of the machine learning algorithm is straightforward, but the selection of well-discriminating features is a challenging task [10].  
Time-series data are a sequence of measurements/observations sequentially taken in time [11]. In the context of stress, time-series data are referred to as a collection of data points over a period that measures the psychological or physiological responses of an individual to any applied stressor. These data points are collected at regular intervals (per second, per minute, or hour) and include measurements of respiratory rate, heart rate, cortisol levels, blood pressure, or a self-reporting stress level. The time-series data are analysed to understand the stress patterns over time, which provides better insights into how an individual's stress levels change in response to different stressors and interventions. The learnings can be then used to develop algorithms or classification models to predict individuals' stress conditions based on their response to stress.  
Considering \(\mathbf{T}\) as a set of time-series data, where \(\mathbf{T}=\left\{\mathbf{F_{i}}\right\}_{i=1}^{\mathbf{n}}\). To use \(\mathbf{T}\) set as an input to supervised or unsupervised classification algorithms, each time-series \(\mathbf{F_{i}}\) must be mapped to a well-defined feature vector with \(\mathbf{X}\) dimensions \((\overset{\rightarrow}{\mathbf{F_{i}}}=(\mathbf{F_{i,1}},\ \mathbf{F_{i,2}},\ldots,\ \mathbf{F_{i,X}}))\)[12]. The most efficient and effective way of feature extraction is to characterize the time-series data into a distribution of data, stationarity, correlation properties, entropy, and non-linear time-series analysis [13; 14; 15; 16]. The extraction of only significant features is vital for both regression and classification tasks, as the irrelevant features will weaken the algorithm's ability to generalize beyond the training set and causes overfitting [17].  
Stress can be defined as a disturbance in the homeostatic balance of the body. Activation of the stress response triggers the sympathetic nervous system and inhibition of the parasympathetic system, which causes the release of stress hormones. These hormones change the heart rate, blood supply to muscles, and respiratory rate and increase cognitive activities to cope with stressors. These stress-specific responses are commonly used to quantitively assess or monitor stress [18; 19; 20; 21]. The statistical analysis of different physiological parameters including muscle activation, skin conductance, skin temperature, brain signals, respiratory rate, heart rate, and their variations, and classification analysis leading to shortlisting of respiratory rate and heart rate was illustrated in [22] and also used by [23; 24].