## 2 Literature Review  
Traditional methods for time series data processing have relied heavily on statistical and digital signal-processing techniques [7]. Statistical approaches, including autoregressive models [8] and integrated moving average models [9], have provided a foundational framework for analyzing linear dependencies within data. Concurrently, digital signal-processing techniques, such as the Fourier transform, have facilitated trend decomposition and offered insights into the underlying patterns of time series data [10; 11; 12]. As technology advanced, the focus shifted towards more nuanced analyses, incorporating non-linear dynamics [13] and time-frequency analysis [14]. Bayesian methods [15] have emerged as powerful tools, incorporating prior knowledge and uncertainty into models, thereby providing robust analyses capable of handling non-Gaussian distributions. In parallel, researchers have addressed challenges in data preprocessing, focusing on mitigating issues, such as noise reduction and feature selection arising from low-quality data [16; 17]. Novel methodologies, such as those proposed by [PERSON] et al. [18], and the use of positional self-attention autoencoders by [PERSON] and [PERSON] [2], have underscored the critical importance of preprocessing in enhancing the performance of time series analyses. The limitations of traditional approaches have catalyzed the development of advanced techniques, especially within the domain of machine learning. Recurrent Neural Networks (RNNs) marked a significant breakthrough in time series analysis due to their capacity to process sequences and retain past information [19]. To further enhance the performance of RNNs, Long Short-Term Memory (LSTM) units [20] and Gated Recurrent Units (GRUs) [21] were introduced. These advancements addressed issues, such as vanishing and exploding gradients, thereby enhancing the networks' efficiency. Additionally, Convolutional Neural Networks (CNNs) have proven to be powerful tools in time series analysis, excelling in recognizing local and global patterns within data [22]. The integration of CNNs with other methods, such as exponential smoothing, has led to hybrid models that offer improved forecasting accuracy. Recent research has focused on leveraging neural network technologies for preprocessing time series data. Pre-trained LSTM-based stacked autoencoders [23] and ensembles of recurrent autoencoders [24] have shown promise in outlier detection and data denoising, highlighting the potential of neural networks for improving data quality for subsequent analyses. Overall, while statistical approaches, like ARIMA, have provided foundational insights, their limitations have driven the adoption of neural networks, such as RNNs, LSTMs, and CNNs, to better capture complex temporal patterns. Recent studies emphasize the complementary strengths of combining statistical and machine learning methods, as seen in the application of these techniques to the Jena Climate dataset. This ongoing evolution underscores the need for continued innovation in time series analysis. The analysis of the Jena Climate dataset by [PERSON] [25] involved a detailed Exploratory Data Analysis (EDA) to understand the dataset's characteristics, followed by the application of an ARIMA model for time series forecasting. This approach predicts future climate conditions based on historical data, which helps to reveal patterns, trends, and correlations in climate variables, such as temperature and humidity. Moreover, the ongoing debate between statistical and machine-learning approaches highlights the dynamic nature of this field, underscoring the necessity for continued research and innovation. The \"Jena Climate Prediction with LSTM\" analyzed by [PERSON] [26] and the \"Daily Forecasting LSTM-FB Prophet\" analyzed by [PERSON] [27] both utilized advanced machine learning techniques for climate data forecasting. [PERSON]'s approach focused on employing Long Short-Term Memory (LSTM) networks.