Enhancing Time Series Data Predictions: A Survey of Augmentation Techniques and Model Performances

Machine Learning approaches are excellent but require a large amount of data which is not easy to get. Data augmentation approaches are used to generate data and improve models’ performance. This study investigates the efficacy of machine learning models in temperature prediction within the domain of climate research, addressing the challenge of limited data through the incorporation of data augmentation techniques. Using a variety of traditional statistical and machine learning models on the "Jena Climate 2009-2016" dataset, this study examines how well data augmentation techniques can increase the accuracy of temperature predictions. Climate research and meteorology are two domains where temperature prediction is essential. However, because climate data is complex and dynamic, getting high precision in such projections is still a difficult task. We assess the efficacy of various models, including the traditional statistical ARIMA model, deep learning models like WaveNet and recurrent neural networks like LSTM, in conjunction with various data augmentation techniques, to address this difficulty. When rotation augmentation and jittering are applied, the ARIMA model significantly improves, indicating how well-suited traditional time series models are to fluctuations in data. Deep learning models, such as LSTM and WaveNet, on the other hand, show strong baseline performance when no augmentation is applied. The analysis reveals that unlike permutation techniques, scaling, Jittering and Rotation consistently improve model performance, emphasizing the critical role of selecting appropriate data augmentation strategies. WaveNet exhibits remarkable consistency in accuracy and precision, surpassing ARIMA, while LSTM demonstrates strong performance even with augmentations. These results offer a thorough understanding of how model design and data augmentation methods interact when predicting time series data. By providing insights that can improve temperature forecast accuracy and advance knowledge in meteorology and related fields, this research adds to the changing field of time series forecasting.


INTRODUCTION
Time series data predictions play a pivotal role in various fields, from temperature and weather forecasting to financial market analysis.Ensuring accurate and reliable predictions is essential for informed decision-making.This study delves into the realm of enhancing time series data predictions through a comprehensive survey of augmentation techniques and model performances.Augmentation techniques involve manipulating and expanding the training dataset to improve model generalization, especially crucial for capturing intricate patterns in time-varying data.The research focuses on evaluating the performance of key models such as Long-Term Memory (LSTM)-based models, WaveNet-based models, and Autoregressive Integrated Moving Average (ARIMA)-based models on temperature datasets.Furthermore, augmentation techniques, such as Jittering, Permutation, Rotation, Scaling, Magnitude Warping, and Window Warping, are systematically applied to investigate their impact on model accuracy and robustness (Source code available at https://github.com/avokhuese/TSdataaugmentation).
However, analyzing and modeling time series data pose unique challenges due to their temporal dependencies, trends, and seasonality.Accurate forecasting, anomaly detection, and classification of time series data require robust models capable of capturing complex patterns and dependencies.
In recent years, there has been a growing interest in the development of augmentation models for time series data [1].These models aim to generate synthetic data that closely resembles real-world time series, thereby augmenting the available dataset and enhancing the performance of downstream tasks [2].Therefore, augmentation techniques offer several advantages, including improving model generalization, addressing class imbalance, and mitigating the limitations of limited or incomplete datasets.
Previously, various approaches have been proposed for time series data augmentation, leveraging different methodologies such as generative adversarial networks (GANs) [1] - [2], recurrent neural networks (RNNs) [3 -5], WaveNet-based models [6], transformerbased models [7,8], and ARIMA-based models [9,13].Each of these approaches has its strengths and limitations, and understanding their characteristics is crucial for designing effective augmentation strategies.For instance, WaveNet-based augmentation models have shown efficiency in generating sequential data point-by-point, capturing fine-grained details and nuances present in the data [6].However, training WaveNet models can be computationally expensive and may suffer from overfitting and generalization issues.These models have been adapted for time series data and show promise in capturing complex patterns and dependencies.However, challenges related to training stability, mode collapse, handling missing or irregular data, and discriminator design need to be addressed to fully exploit their potential for time series data augmentation.
Given the diverse range of augmentation models available, it is crucial to understand their strengths and limitations to select appropriate methodologies for specific time series data analysis tasks.This study aims to provide a comprehensive analysis of different augmentation models, including Long Short-Term Memory (LSTM)-based models, WaveNet-based models, and Autoregressive Integrated Moving Average (ARIMA)-based models.By examining the challenges, limitations, and potential solutions associated with each approach, this study aims to contribute to the development of robust and efficient time series data augmentation techniques.By identifying strengths, weaknesses, and challenges associated with each approach, the study lays the foundation for future research endeavors to further enhance the accuracy and applicability of time series predictions.
In general, this study contributed to the enhancement of time series data prediction/augmentation in the following ways: • State-of-the-art survey: providing a clear overview and stateof-the-art of existing time series models for data augmentation, indicating their strengths and weaknesses.• Data augmentation analysis: by applying seven different data augmentation techniques -original, jittering, rotation, permutation, window slicing, scaling and magnitude warping, the study explored their impact on time series prediction.The study suggests the potential for future research in hybrid approaches, emphasizing the benefits of combining sophisticated models like LSTM and ARIMA to address their individual deficiencies and improve overall forecasting performance.
The rest of this paper is structured such that section II details succinctly the state-of-the-art of the top three data augmentation models for time series prediction.It also provides a summary of the comprehensive comparison of the top three models based on their features and capabilities.Section III and IV describe the enhancement of the augmentation models, capturing a clear description of the sample temperature dataset, the data augmentation techniques and performance evaluation of the selected models.It also presents the experimental results from evaluating the model's performance in predicting the temperature time series data from Jena Climate 2009 -2016 datasets.Section V presents the experimental evaluation of each data augmentation model with a view to showcase their time series predictive capabilities.While the last section, Section VI presents the discussion, conclusions and provides future directions for the study.

TOP DATA AUGMENTATION MODELS AND CHALLENGES
This section provides a detailed description of the top data augmentation models while succinctly presenting the various challenges accompanying the use of the individual models.In section A, the ARIMA model is described, capturing the challenges in using the traditional time series model.While in section B and C, the deep learning modern time series models, Wavenet and LSTM respectively are presented indicating the descriptions and challenges in the use of the models for time series predictions.

ARIMA-based Augmentation Models
The ARIMA-based model stands as a stalwart in time series analysis and forecasting, providing a powerful tool for understanding and predicting sequential data patterns.ARIMA integrates autoregressive (AR) and moving average (MA) components, coupled with differencing to address non-stationary time series data.Its primary function is to capture and model the linear dependencies and trends inherent in time-ordered datasets.By leveraging past observations and the errors from previous predictions, ARIMA excels at capturing both short-term fluctuations and long-term trends in data.Its versatility and simplicity have made it a widely adopted model in various domains in meteorology and beyond.The effectiveness of ARIMA lies in its ability to distill complex temporal patterns into a manageable framework, enabling seamless predictions and decisions based on historical trends and observations.Fig. 1. provides a visual representation of the ARIMA-based model architecture and describes how historical datasets yield to final prediction of a time series.The framework architecture indicates that time series data is collected for data pre-processing and feature selection before being passed on to the model for training before prediction is done.Basically, an ensemble prediction based on the capacity of the model is done from the multiple layer perceptron, multilinear regression and the ARIMA model itself to produce a weighted average of the combined output for the final prediction.ARIMA-based augmentation models have been widely used for synthetic data augmentation, but they also come with certain limitations that need to be addressed for their effective application such as: • Inability to capture patterns: ARIMA-based augmentation models have limited capability to capture complex nonlinear patterns and dependencies in time series data [11,14].The ARIMA model is based on linear assumptions and may struggle to capture and represent nonlinear relationships in the data accurately.This limitation can affect the model's ability to generate synthetic data points that closely resemble the underlying patterns and dynamics of the real data.• Over-reliance on data availability: The reliability of ARIMAbased augmentation models heavily depends on the availability and quality of the training data [15 -17].Insufficient or incomplete datasets can pose challenges for the ARIMA model, as it relies on historical data to estimate parameters and make forecasts.In scenarios where data is limited or missing, the accuracy and reliability of the augmentation model may be compromised.• Privacy and Security Concerns: Another challenge associated with ARIMA-based augmentation models is the privacy and security concerns related to sensitive data.For example, when using the model for forecasting the number of patients in an epidemic disease, privacy regulations and data security issues can limit the availability and use of medical data for training and evaluation [11,14].These constraints can impact the model's performance and restrict its application in certain domains.
Evaluating the accuracy of ARIMA-based forecasted results is crucial for assessing the performance of the augmentation model.Metrics such as mean absolute error (MAE), root mean squared error (RMSE), or forecast error plots are commonly used to evaluate the forecast accuracy [12,18].It is important to consider these metrics to gauge the effectiveness of the ARIMA-based augmentation model and identify any potential shortcomings or discrepancies between the generated synthetic data and the real data.
The inability of ARIMA-based augmentation models to enhance forecast accuracy in unpredictable or highly volatile time series data poses a significant challenge.These models may struggle to capture and reproduce the inherent uncertainty and variability in such data, leading to less reliable forecasts and increased uncertainty in decision-making.While ARIMA-based augmentation models have been widely used [19], they face challenges in capturing complex nonlinear patterns, relying on available and complete data, addressing privacy and security concerns, and accurately forecasting unpredictable or highly volatile time series data.

WaveNet-based Augmentation Models
WaveNet, represents a revolutionary advancement in time series prediction, particularly in the realm of sequential data generation.Unlike traditional models, WaveNet leverages deep neural networks known as dilated causal convolutions, enabling it to capture intricate temporal patterns in data.
This model excels in generating high-fidelity sequences, making it particularly effective in applications like speech synthesis and music generation.In time series prediction, WaveNet showcases its versatility by modeling complex dependencies within sequential data, allowing it to make accurate and contextually rich predictions.Its success lies in its ability to generate waveform samples directly, providing a finer level of detail compared to other models.The model's architecture in Fig. 2 utilizes dilated convolutions, which expand its receptive field without an increase in parameters, making it computationally efficient.
WaveNet-based augmentation models have shown significant potential in various domains and has been widely adopted in various fields, including climate modeling, where its capacity to capture nuanced temporal variations makes it a powerful tool for forecasting temperature and other meteorological variables, air quality evaluation, and urban air quality prediction [6,22].However, there are certain limitations and considerations that need to be addressed for the effective implementation and utilization of WaveNet-based augmentation models such as: • Computationally expensive: training WaveNet models can be computationally expensive due to their autoregressive nature, which requires generating each data point based on previous data points.This computational complexity can limit the scalability and practical deployment of WaveNetbased augmentation models, especially when dealing with large-scale or high-dimensional datasets.• Over-reliance on training data: another challenge associated with WaveNet models is their reliance on the training data to capture underlying patterns and dependencies, which can lead to overfitting and limited generalization [22].WaveNet models require a substantial amount of diverse and representative training data to effectively learn the data distribution and generate realistic samples.Insufficient or biased training data may result in a lack of diversity in the augmented data, reducing its quality and limiting its applicability in real-world scenarios.• Inability to model long-term dependency: While WaveNetbased augmentation models excel in capturing short-term dependencies in the data, they may struggle to model longterm dependencies effectively [22].This limitation can impact the generation of time series data that requires a broader context or exhibits complex long-term patterns.It is crucial to explore techniques that can enhance the modeling of long-term dependencies in WaveNet-based augmentation models to improve their performance and applicability in a wide range of time series tasks.
Furthermore, the design of robust and effective WaveNet-based augmentation models requires finding a balance between computational efficiency and the quality of augmented data.Developing more efficient training algorithms or exploring parallel computing techniques can alleviate the computational burden and enable the practical use of WaveNet-based augmentation models in real-time applications.While WaveNet-based augmentation models offer the advantage of generating high-fidelity sequential data, they face challenges related to computational efficiency, reliance on training data, modeling long-term dependencies, and balancing computational complexity with data quality.Addressing these limitations through advanced techniques and methodologies will contribute to the broader adoption and effectiveness of WaveNet-based augmentation models in various domains.

LSTM-Based augmentation Models
The LSTM is a type of recurrent neural network (RNN) designed to address the vanishing gradient problem, a common challenge in traditional RNNs.LSTMs excel in capturing long-term dependencies and temporal patterns within sequential data.Unlike standard RNNs, LSTMs possess specialized memory cells and gating mechanisms, allowing them to selectively retain and forget information over extended sequences.
This unique architecture enables LSTMs to effectively learn intricate relationships within time series data, making them particularly well-suited for applications such as speech recognition, natural language processing, and time series forecasting.Their versatility, adaptability to various domains, and superior performance in handling complex temporal dependencies have positioned LSTMs as a cornerstone in contemporary deep learning applications, contributing significantly to advancements in predictive modeling and sequence analysis.
Hence, LSTM models have gained significant attention for their effectiveness in time series analysis and prediction tasks [20 -24].One of the key advantages of LSTM models is their ability to capture long-term dependencies in time series data, which is crucial for accurate prediction and forecasting [20,24].However, there are certain challenges associated with LSTM models that need to be addressed for improved performance and broader applicability such as: • Computationally expensive: training LSTM models can be computationally expensive, especially when dealing with large-scale or high-dimensional time series datasets.Efficient implementation and optimization techniques are necessary to mitigate these computational challenges and enable practical deployment of LSTM models.LSTM models are particularly well-suited for anomaly detection in time series data [21].• Over-reliance on labeled training data: The LSTM model shows potential to learn complex patterns and deviations from normal behavior, making them valuable for identifying abnormal events or outliers.However, the performance of LSTM-based anomaly detection heavily relies on the availability of labeled anomaly data for training.Obtaining a sufficiently diverse and representative labeled dataset can be challenging in many real-world applications, limiting the generalizability and effectiveness of LSTM-based anomaly detection models.• Limited adoption in varying domains: in the application of LSTM models to specific domains, such as petroleum science, hydrology, or energy analysis, has shown promising results [22,24].However, the performance and applicability of LSTM models may vary across different domains and datasets.Further research is needed to understand the specific requirements and limitations of LSTM models in various application domains and to develop domain-specific enhancements or adaptations.
Therefore, although LSTM models offer valuable capabilities for time series analysis, including capturing long-term dependencies and detecting anomalies, there exist challenges related to computational efficiency, availability of labeled anomaly data, generalizability, and domain-specific adaptability should be addressed to fully leverage the potential of LSTM models in diverse time series tasks and applications.Fig. 3 indicates the visual representation of the LSTM model architecture.The architecture is described based on the memory cells from the forget gate to the output gate through the input gate of the model framework.
Table 1 presents a comprehensive comparison of three top time series augmentation models: ARIMA, WaveNet, and LSTM.Each model is evaluated across key features such as handling nonlinearity, data requirements, forecasting applications, adaptability to domains, computational efficiency, real-time applications, and interpretability.The table utilizes ticks ( ) (Green: Strong applicability; Yellow: Weak applicability) to signify the presence or applicability of a feature for each respective model and (x) to indicate lack of applicability of a feature.4, capturing their challenges in time series data augmentation.For instance, TTS-GAN augmentation models offer promising opportunities for generating synthetic time series data.However, it shows difficulty in generating diverse and highquality samples.Mode collapse and instability during training can limit the model's ability to capture the full diversity of the real data distribution [25,26].Also, one of the most robust TS augmentation models, VAE-based augmentation models have gained attention for their ability to generate synthetic data that captures the underlying distribution and characteristics of the original time series data.Several studies [27][28][29][30] have explored the application of VAE-based augmentation models in different domains, addressing challenges such as anomaly detection, classification performance improvement, irregularly sampled time series, and sparse datasets.Similarly, RNN-based augmentation models have been widely explored and applied in various domains for time series forecasting, data handling, and classification tasks [31][32][33][34][35].While RNN-based models offer these advantages, they also present some limitations that need to be considered for effective utilization.One of the challenges associated with RNN-based augmentation models is the handling of multiple short time series and the preprocessing of irregular time series data.TimeGAN models have shown promise in generating time series data with high fidelity and capturing finegrained details and nuances [36,37].However, a challenge with TimeGAN is that it generates long time series due to the sequential nature of the generator and the short memory of LSTM cells [36,37].This limitation can impact the ability of TimeGAN to capture long-term dependencies in the generated data and may result in a loss of temporal coherence over extended periods.This column provides the potential temperature in Kelvin (K).Potential temperature is a measure of air temperature that is adjusted for changes in pressure, making it useful for analyzing air masses.

ENHANCING TIME SERIES DATA PREDICTIONS
In this section we look at the different data augmentation techniques on the datasets.In Section A, the detailed description of the Jena Climate 2009 -20016 datasets selected for prediction is presented.While in Section B, a state-of-the-art of the various data augmentation techniques is presented with proposed parametric scaling factors to be adopted.In Section C, the performance evaluation technique is further elaborated underpinning the procedure of use in the evaluation of the error measurements and performance metrics for the models' predictions.

Dataset Description
The dataset used for this study is from the "Jena Climate 2009-2016" dataset repository which contains detailed weather measurements recorded by a meteorological station in Jena, Germany, over a span of eight years.The indicators and corresponding descriptions of relevant variables in the dataset is presented in Table 2 below.

Augmentation Techniques
To appropriately evaluate the time series augmentation performance of each of the methods, we used the seven general time series data augmentation techniques as described in [38].In this case, the parameters deduced for each of the Time Series augmentation methods were set to be like the ones used in the respective literature.
The study used the following comparison methods for evaluating the performance of the time series augmentation methods: • None/Original: This method uses the original time series without augmentation for a baseline.• Jittering (Jit): Aside from the original data transformation, one of the most effective and yet simplest transformationbased data augmentation techniques is the jit technique which adds noise to the time series.Jittering can be defined as: where ∈∼ (0, 2 ) is an ideal Gaussian noise which is added at each step of time t.In this case, a random noise from a Gaussian distribution with a mean = 0 and standard deviation = 0.02 is added to the original time series.
• Scaling (Scal): Here we increase and decrease the magnitude of all elements in the time series by a scalar factor.Suppose the scaling parameter multiplies the entire series, then scaling is defined as: The scaling parameter is determined by a Gaussian distribution ∼ (1, 2 ) while is the hyperparameter [33].In scaling, we picked the scalar by a Gaussian distribution with mean = 1 and standard deviation 0.2.
• Magnitude Warping (MagW): This technique warps a signal's magnitude by a smoothed curve.MagW is defined by: = 1 1 , . . ., , . . ., where 1 , . . ., , . . ., serves as the created sequence for the purpose of interpolating a cubic spline ( ) and the knots = 1 , . . ., , . . ., .Each of the knots is taken from distribution ( , 2 ) where the hyperparameters are defined by the knot numbers and standard deviation .Here, the magnitude of each time series is multiplied by a curve created by cubic spline with four knots at random magnitudes with = 1.1 and standard deviation = 0.3.
• Flipping/Rotation (Rot): Taking the patterns in the time series are univariate, patterns are randomly flipped.Rotation is defined as: where is a random rotation matrix for angle ∼ (0, 2 ) which forms an effective rotation angle for multivariate time series data and flipping for the univariate time series dataset [33].
• Permutation (Perm): In this method, the time series is adjusted randomly and selected given a set is already selected.It is basically aligned around a given selected pattern.Although permutation does not preserve time dependencies, it can be performed with equal sized segments and with variable sized segments.While the equal sized segments split the time series into number of segments of length and permutes them and variable sized segments uses the random sizes of segments.
• Window Slice (WinW): In window slicing, a window of 90% of the original time series is chosen at random.In this case, the data is augmented by slicing time steps of the series off the ends of the pattern.
= , . . ., , . . ., + , where is the size of a window and is a random integer such that 1 ≤ ≤ − .Jittering introduces variability with a maximum value of less than 0.02 as indicated in Table 3.It only modifies the data somewhat, adding a little bit of noise but not enough to fundamentally change the underlying patterns.With a maximum scaling factor of less than 0.2, scaling enhances the data.This shows that the method somewhat resizes the data points, possibly maintaining the dataset's general form and trends.Variations with a magnitude shift of 0.3 are introduced by magnitude warping.This might have an impact on the amount of data, but it does not seem to bring about any significant changes.Data is shuffled using a rate of less than 0.03 in permutation.This may make it difficult for the model to identify stronger patterns and drastically alter the initial sequence of the data points.Changes are introduced by flipping or rotating, and their maximum value is less than 0.02.This suggests slight changes in direction or orientation that might not significantly affect the data.Window Slice modifies the data by changing the window size by 0.05.This could somewhat change the modeling-related data granularity.Time warping is adjusting the data by changing the number of knots by 0.3.This implies that the method modifies the data's temporal structure.In terms of DTW (Dynamic Time Warping) limitations, Window Warping introduces modifications with a constraint of 1.5.The alignment of the data points inside the time series can be significantly altered using this technique.

Performance Evaluation
This section captures the model performance metric for 7 time series prediction models, evaluating their errors of prediction as well as accuracy and precision in predicting future time series for temperature datasets.
In evaluating the performance of the selected models on time series data, the metric synopsis is presented.
Equations 6), ( 7) and ( 8) represent the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) respectively and are commonly used metrics to evaluate the performance of models in various fields, including time series prediction.These metrics assess the accuracy of predictions by comparing the predicted values ( ˆ ) to the actual values ( ).These metrics are used in the context of time series prediction models to quantify the accuracy and performance of the models on the temperature datasets.Lower values of MSE, RMSE, and MAE indicate better model performance, as they imply smaller differences between predicted and actual values.The use of these metrics involves computing them for each prediction in the dataset and then averaging the results to obtain a comprehensive evaluation of the model's predictive accuracy.
Table 4  models over a wide range of criteria, demonstrating its greater capacity for precise temperature prediction.Excellent performance is also shown by WaveNet and VAE models.In contrast, TimeGAN performs the worst according to a few measures, indicating its shortcomings in temperature prediction.Each model is evaluated based on the movable variables in Table 3.Therefore, if we utilize the RNN model, the data augmentation strategy VAE is appropriate for the "Jena Climate 2009-2016" dataset.

DATA AUGMENTATION TECHNIQUES AND PERFORMANCE
This section is a summary of the data augmentation and performance techniques for the selected models.In section A, we present the results of error analysis of the various augmentation techniques for the models' selections as applied to the temperature datasets.Section B also captures the various performance metrics such as accuracy and precision of the models in relation to the applied augmentation techniques for the time series data.

Error measurement in Augmentation
This section as indicated in Table 5 captures the error metric of each selected model with corresponding data augmentation techniques.It describes the variation of the models with or without the application of the various augmentation techniques in predicting the temperature time series data.Table 5 shows that ARIMA's error metrics are quite high for all augmentation strategies, meaning that none of the augmentations significantly improve the model's performance.The greatest error metrics for MAE, RMSE, and MSE are produced by the "None" augmentation, which stands for the unaugmented model.This indicates that ARIMA's base performance for temperature prediction is not very good.With the lowest MAE, RMSE, and MSE values among the augmentation approaches, "Scaling" seems to be the most successful at lowering error measures.It would appear from this that rescaling the data enhances performance.It is important to note that, irrespective of the augmentation method used, ARIMA Conversely, WaveNet exhibits low error metrics in its unaugmented state and performs effectively even in the absence of augmentation.Minor changes are introduced using augmentation approaches, while the fundamental functionality of the model is maintained, with minimal error levels.As one of the finest models for temperature prediction, WaveNet is not greatly affected by augmentation approaches, even though they do have an impact on error metrics.Even in the absence of augmentation, LSTM performs well and has comparatively low error metrics, suggesting that it is a suitable model for temperature prediction tasks.The results indicate that "Scaling" and "Magnification and Warping" (MagW) are the most effective augmentation approaches in improving the model's precision, with only slight gains observed in terms of MAE and RMSE.
Even with relatively little performance modifications brought about by augmentations, LSTM continues to be a reliable performer.The model architecture selection has a significant impact on the models' performance.ARIMA's accuracy in predicting temperature is limited, as evidenced by its persistent rising error metrics.Error metrics show very slight gains when data augmentations are applied to WaveNet and ARIMA.However, LSTM shows its adaptability and resilience by operating effectively even in the absence of augmentation.The "Scaling" and "Magnification and Warping" augmentation approaches seem to be the most successful at enhancing error metrics for ARIMA and LSTM.Even so, LSTM is one of the most effective models for predicting temperature, both with and without data augmentations.

Performance Metric in Augmentation
In this section, the performance metric which includes the accuracy and precision of the models' predictions with or without the application of the various augmentation techniques is indicated as seen in Table 6.Here, the results showcase how effective with near proximity the various models can predict future trends in the temperature datasets when it is augmented and when it is not.Table 6.When ARIMA is first unaugmented, its accuracy and precision are comparatively lower than those of other models, with values of 93.8% and 96.5%, respectively.With accuracy and precision values reaching 98.3% and 98.9% or higher, respectively, the "Jit" and "Rot" augmentations greatly improve ARIMA's performance and demonstrate the model's strong impact from jittering and rotation augmentation.The accuracy and precision of ARIMA are only slightly improved by other augmentation methods.Even with these enhancements, ARIMA still performs worse than other models following augmentation.In its most basic version, WaveNet has remarkable performance, achieving high accuracy and precision of 98.7% and 98.9%, respectively.While some slight differences are introduced by augmentation techniques, the model's basic performance is maintained, with consistently high levels of accuracy and precision.When it comes to temperature prediction, WaveNet is among the top models, whether data augmentations are included.The model is a trustworthy option because it consistently maintains excellent precision and accuracy.
However, even in the absence of augmentation, LSTM shows good accuracy and precision values of 99.2% and 98.7%, respectively.Although there are slight variations in accuracy and precision with the "Scal" and "MagW" augmentations, the core performance is still strong.Other augmentation methods only have a small effect on precision and accuracy.LSTM is a dependable option for temperature prediction since it keeps up its good performance, maintaining high levels of accuracy and precision.As a result, the selection of model architecture and data augmentation strategies has a substantial impact on the accuracy and precision of model performance.Even with augmentation, ARIMA's accuracy and precision are still inferior to those of other models.In contrast, both WaveNet and LSTM exhibit excellent accuracy and precision and continue to function well even after data augmentations.With or without data augmentation, WaveNet routinely ranks among the top-performing models for temperature prediction, giving it a dependable option for precise and accurate predictions.Its applicability for temperature forecasting jobs is further demonstrated by the fact that LSTM retains its effectiveness with only slight variations caused by augmentation.

EXPERIMENTAL EVALUATION OF DATA AUGMENTATION MODELS
This section provides visualizations of model performance of model selections.Here we have shown charts of different models ARIMA, LSTM and WaveNet for time series data augmentation and forecasting.The section provides relevant charts of temperature data from the Jena Climate 2006 -2016 datasets in comparison with the augmentation of the model predictions.

ARIMA Augmentation Model and Techniques
This section shows 7 figures and Fig. 4 6.Nevertheless, augmentation methods greatly improve its performance, especially "Jit" (Jittering) and "Rot" (Rotation).The model's flexibility is demonstrated via Jittering and Rotation, which add variants that enhance accuracy and precision.Even with these augmentations, ARIMA's performance is still better than that of other models, indicating that it may not be able to fully capture the underlying patterns in the dataset.

A. WaveNet Augmentation Technique
Using the WaveNet augmentation technique, this section shows 7 figures and Fig. 5  Similarly, with the base form performance metrics indicated in Table 6 and displayed in Fig. 5 (a -g), it demonstrates a WaveNet deep learning model that excels in predicting temperature data from the Jena Climate 2009 -2016 datasets.It exhibits high precision (98.9%) and accuracy (98.7%) in its base form as indicated in Table 6 and shown in  6.While modest differences are introduced by augmentation techniques, the essential performance of the model is unaffected.Both with and without data augmentations, WaveNet continues to rank among the top models.This robustness demonstrates how well the model can adjust to the noise and variations present in temperature data.The usefulness of deep learning models in time series forecasting applications is also emphasized, as they can benefit from the architecture's depth and flexibility to accommodate different data situations.

LSTM Augmentation Technique
This section shows 7 figures and Fig. 6  As indicated in Table 6 and shown by Fig. 6 (a -g), LSTM performs well at first with no augmentation technique applied as indicated in Fig. 6 (a) and achieving 98.7% accuracy and 99.2% precision, which is captured in Table 6.Furthermore, except for the remaining augmentation techniques as indicated in Fig 6 .(b), (c), (d), and (f), only test performance of the augmentation technique as indicated in Fig. 6 and as shown in Table 6.(e) performed poorly.The fundamental performance of the model is not considerably lowered by augmentation techniques like "Scal" (scaling) and "MagW" (magnification and warping), however they do introduce small variations in accuracy and precision.Because it does not lose its dependability, LSTM is a good option for temperature prediction applications.The resilience of the model is highlighted by its ability to handle both original and augmented data while retaining excellent accuracy and precision.This investigation further emphasizes the advantages of deep learning models for time series forecasting, especially in terms of their ability to adjust to changing data properties and augmentations.

DISCUSSION, CONCLUSION AND FUTURE DIRECTIONS
This study focuses on enhancing temperature prediction accuracy using the "Jena Climate 2009-2016" dataset through the exploration  5 and 6).The analysis reveals that unlike permutation techniques, scaling, Jittering and Rotation consistently improve model performance, emphasizing the critical role of selecting appropriate data augmentation strategies.WaveNet exhibits remarkable consistency in accuracy and precision, surpassing ARIMA, while LSTM demonstrates strong performance even with augmentations.The study contributes valuable insights for time series forecasting, showcasing the potential of modern deep learning models, especially when supported by suitable data augmentation.It sets a standard for future predictive modeling, emphasizing the significance of combining sophisticated models with augmentation for enhanced accuracy.However, challenges arise in choosing the right augmentation strategy, performance metrics, and balancing the trade-offs between accuracy, precision, and computational resources.Dataset attributes, computational expenses, and the risk of overfitting further complicate the decision-making process.Striking a balance between improved performance and available resources remains a challenge, necessitating a nuanced understanding of the application context for optimal augmentation strategy selection.This study thus highlights the complexities involved in leveraging data augmentation for enhanced predictive modeling and emphasizes the need for careful consideration and contextual understanding in its application.The Jena Climate dataset presents a complex time series scenario, highlighting specific deficiencies in the standalone performance of time series data augmentation models such as the deep learning model WaveNet, the recurrent neural network LSTM model, and the traditional time series model ARIMA (see Table 5 and 6).The challenges identified encompass a spectrum of issues within these models.LSTM models, for instance, grapple with challenges related to computational efficiency, where their application to large datasets can be resource intensive.Another hurdle involves the scarcity of labeled anomaly data, crucial for effective anomaly detection using LSTM models, and the imperative need for enhanced generalization and domain-specific adaptability.In a parallel vein, WaveNet models encounter obstacles, including time consumption and computational complexity, making them expensive to deploy.These models also exhibit a propensity for over-dependence on the training dataset.On a different note, ARIMA models present a unique set of challenges.Their struggle lies in the inability to capture intricate nonlinear patterns and their heavy reliance on complete data, raising privacy and security concerns.Additionally, ARIMA models face accuracy issues when forecasting unpredictable or highly volatile time series data.These diverse challenges underscore the multifaceted nature of obstacles faced by different models, emphasizing the importance of addressing specific issues tailored to each model's characteristics and application domains.These challenges are particularly pronounced in the context of the Jena Climate dataset, where the intricate patterns and dependencies in climate data demand a sophisticated and adaptable modeling approach.Given these shortcomings, a future hybrid study using ensemble stacking becomes imperative.Ensemble stacking offers a strategic solution by combining the unique strengths of time series augmentation models such as LSTM and ARIMA.Future approaches should aim to mitigate computational challenges, enhance generalization and adaptability, address anomaly detection issues, and integrate diverse augmentation techniques, all of which are critical for achieving improved forecasting accuracy specifically tailored to the complex characteristics of the Jena Climate dataset.The ensemble stacking strategy provides a comprehensive solution to the dataset-specific challenges, creating a synergistic modeling framework that capitalizes on the strengths of both LSTM and ARIMA models to enhance overall performance in time series prediction tasks.
In the context of future datasets with similar features to the temperature datasets in the Jena Climate dataset, the application of models such as the recurrent neural network model LSTM and the traditional ARIMA demonstrates the potential for efficient and effective forecasting.The models, when trained on datasets with comparable characteristics, can leverage their capabilities to capture long-term dependencies and intricate patterns, showcasing improved performance and accuracy.However, the deficiency becomes apparent when transitioning to a different domain with distinct features and patterns.For instance, if the models trained on temperature datasets are applied to a dataset from a different domain, such as financial data or industrial processes or a different sensor dataset with varying timestamps, the inherent deficiencies in generalization and adaptability may impede their effectiveness.The complex and domain-specific patterns in the new dataset may challenge the models' ability to extrapolate meaningful insights, resulting in suboptimal forecasting accuracy.Thus, while these models excel in specialized domains like climate data, their efficiency becomes deficient when confronted with diverse datasets from unrelated domains (finance, agriculture etc) and timestamps (different sensor datasets), necessitating the exploration of hybrid models or domain-specific adaptations for improved performance.
(a) indicates the original temperature time series data from the Jena Climate 2006 -2016 datasets and Fig 4 (b) indicates the chart of the jittered series of the original series while Fig. 4 (c) and Fig. 4 (d) shows the charts of the rotation and scaling series of the original time series respectively.Furthermore, Fig. 4. (e), Fig. 4 (f) and Fig. 4 (g) shows charts of the time series with applied permutation, magnitude warping and window slicing respectively using the ARIMA augmentation technique.ARIMA, a conventional time series forecasting model, first performs moderately in Fig.4(a) with an accuracy of 93.8% and a precision of 96.5% as shown in Table6and performs poorly in Fig.4(e) as compared to better performance of techniques in Fig 4. (b), (c), (d), and (f), based on the "Jena Climate 2009-2016" dataset as indicated by the accuracy and performance scores in Table (a) indicates the original temperature time series data from the Jena Climate 2006 -2016 datasets and Fig 5 (b) indicates the chart of the jittered series of the original series while Fig. 5 (c) and Fig. 5 (d) shows the charts of the rotation and scaling series of the original time series respectively.Furthermore, Fig. 5. (e), Fig. 5. (f) and Fig. 5 (g) shows charts of the time series with applied permutation, magnitude warping and window slicing respectively.
Fig. (a), performing as indicated in Table 6 and Fig. 5 (e) and showing promising results as indicated in the Euclidean distance/difference between the actual and predicted set of augmentation techniques in Fig 5 (b), (c), (d), and (f) as indicated in Table (a) indicates the original temperature time series data from the Jena Climate 2006 -2016 datasets and Fig 6 (b) indicates the chart of the jittered series of the original series while Fig. 6 (c) and Fig. 6 (d) shows the charts of the rotation and scaling series of the original time series respectively.Furthermore, Fig. 6. (e), Fig. 6 (f) and Fig. 6 (g) shows charts of the time series with applied permutation, magnitude warping and window slicing respectively using the LSTM augmentation technique.

Figure 4 :
Figure 4: Visualization of the Jena Climate dataset using ARIMA and compared augmentation techniques.

Figure 5 :
Figure 5: Visualization of the Jena Climate dataset using WaveNet and compared augmentation techniques.

Figure 6 :
Figure 6: Visualization of the Jena Climate dataset using LSTM and compared augmentation techniques.

Table 1 :
Comparison of Top Augmentation Models

Table 2 :
Description of Jena Climate Datasets

Table 4 :
MODEL PERFORMANCE METRIC presents a comparison of different models' performance indicators, showing clear variations in the models' accuracy and predictive power.With an RMSE of 4.1594 and an MAE of 3.0949, ARIMA performs moderately, suggesting some prediction inaccuracy.However, it gets a commendable Precision, Recall, and F1-Score in addition to a high Accuracy of 93.8%.With the greatest MAE and RMSE values, TTS-GAN performs less well than other models, showing comparatively bigger mistakes in temperature prediction.Its precision is lesser, and its accuracy is 80.8%.Because there is little fluctuation in the predictions, the Recall is extraordinarily high, giving in a high F1-Score.With extremely low MAE and RMSE values, VAE performs better.The results show a great Precision, Recall, F1-Score, and excellent Accuracy of 98.2%.The prediction error minimization of this model is excellent.Low MAE and RMSE estimates are produced by WaveNet, which is very accurate.It achieves a good Precision, Recall, and F1-Score, as well as a 98.7% Accuracy.The greatest MAE, RMSE, and MSE are displayed by TimeGAN, showing significant prediction mistakes.The F1-Score is nonsignificant because the Accuracy is noticeably low (19.2%), and Precision and Recall are both 0%.With a high accuracy of 98.7%, low MAE and RMSE, and great precision, recall, and F1-score, LSTM performs well.It exhibits dependable temperature forecasting abilities.With a high accuracy of 99.7% and the lowest MAE, RMSE, and MSE, RNN is clearly the best-performing model.It attains remarkable Precision, Recall, and F1-Score, indicating precise and dependable temperature forecasts.The RNN model performs better than all other

Table 5 :
AUGMENTATION ERROR METRIC FOR SELECTED MODELS

Table 6 :
MODEL PERFORMANCE METRIC VERSUS AUGMENTATION TECHNIQUES FOR SELECTED MODEL