# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
## 2 Related Work
Furthermore, some other related research has been recently presented in the literature. Common approaches for time series forecasting include statistical methods, machine learning, and deep neural networks. Statistical methods use mathematical and probability analysis to model time series data based on past trends. Common time series forecasting techniques make up the autoregressive model, autoregressive moving average (ARMA) model, and differential autoregressive moving average (ARIMA) model. [PERSON] et al. [45], [PERSON] et al. [46], and [PERSON] [47] have investigated the integration of statistical models \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Parameters Measured** & **Short Description** & **Reference** \\ \hline UCI Air Quality & CO, NOx, NMHC, Benzene, NO\({}_{2}\), O\({}_{3}\) & \begin{tabular}{c} A dataset collected from an air quality \\ monitoring station in Italy includes outdoor and indoor air quality data. \\ \end{tabular} & [35; 36] \\ \hline Berkeley Indoor Air Quality & CO\({}_{2}\), temperature, humidity, PM2.5, TVOC & \begin{tabular}{c} A dataset collected from multiple San Francisco \\ Bay-area indoor environments includes homes, schools, and offices. \\ \end{tabular} & [37] \\ \hline Office Building Energy & Temperature, humidity, CO\({}_{2}\), occupancy, lighting & \begin{tabular}{c} A dataset collected from an office building in the \\ US includes data from multiple floors and \\ rooms. \\ \end{tabular} & [38; 39] \\ \hline UCI Human Activity & \begin{tabular}{c} A dataset collected from wearable \\ accelerometers includes data on human activity \\ recognition in indoor environments. \\ \end{tabular} & [40] \\ \hline DEBS & Temperature, humidity, CO\({}_{2}\) & \begin{tabular}{c} A dataset collected from a New Hampshire, \\ USA, residential building includes data from \\ multiple rooms and floors. \\ \end{tabular} & [41] \\ \hline NA & \begin{tabular}{c} Thermal comfort, air quality, \\ and visual environment \\ \end{tabular} & \begin{tabular}{c} A dataset collected from 20 green office \\ buildings in China. \\ \end{tabular} & [42] \\ \hline UC Berkeley Thermal & Temperature, humidity, air & \
\begin{tabular}{c} A dataset collected from experiments on human \\ thermal comfort in different indoor \\ environments includes data on occupant comfort \\ in different temperature and humidity \\ conditions. \\ \end{tabular} & [43] \\ \hline NA & CO\({}_{2}\), temperature, humidity, TVOC &
\begin{tabular}{c} A dataset collected from multiple indoor \\ environments in the US includes data from \\ homes, schools, and offices. \\ \end{tabular} & [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Available environmental datasets. with backpropagation neural networks to enhance predictions for various applications, including wind power, cloud coverage, and power generation. Machine learning techniques are better suited for nonlinear fitting problems because they adjust model parameters through internal iterations. [PERSON] [48] proposed a rough set backpropagation model for short-term load prediction, which mitigates the effect of noise on prediction accuracy. Multilayer perceptron (MLP) [49], support vector machine (SVM) [50], and hidden Markov models [51] are additional machine learning techniques used for time series forecasting. DNNs [52] also have facilitated the handling of intricate data. DNNs have exhibited exceptional performance in diverse applications, including fault detection, speech recognition, natural language processing (NLP), and disease diagnosis. RNNs have strong nonlinear fitting abilities due to their ability to establish connections in the hidden layers that consider the temporal aspects of the data. Traditional RNNs [53] encounter vanishing gradients, which hinder their ability to capture long-term dependencies. LSTMs and GRUs [54] have been introduced in recent years to address this limitation. LSTMs address gradient vanishing and long-term dependency issues in RNNs by using multiple gated structures.