Non-stationary Data Research Articles

IMPLEMENTATION OF VECTOR AUTOREGRESSIVE (VAR) AND VECTOR ERROR CORRECTION MODEL (VECM) METHOD IN PNEUMONIA PATIENTS WITH WEATHER ELEMENTS IN PANGKALPINANG CITY

The news about Covid-19 is no longer as scary as in previous years. As COVID-19 cases decrease, health protocols are becoming more relaxed, making it easier for the virus to spread. Besides COVID-19, ARI is one of Indonesia's leading causes of death for children under five. Around 20-40% of hospital admissions are children due to ARI, with around 1.6 million deaths due to pneumonia alone in children under five per year. Currently, ARI dominates the diseases most suffered by the people of Bangka Belitung. Based on this, using the VAR and VECM method to analyze pneumonia sufferers in toddlers regarding weather elements in the Pangkalpinang City. The VAR model has a simpler structure with a minimal number of variables where all the variables are endogenous, with the independent variable being the lag. Meanwhile, VECM can be used to model cointegrated and non-stationary time series data. The data used in this research is the number of monthly cases of toddlers suffering from pneumonia and data on climate conditions, namely rainfall, air temperature, air humidity and duration of sunlight during 2019-2021 in Pangkalpinang City. The results of the Granger Causality test show that the pneumonia variables regarding rainfall, temperature, duration of sunlight and humidity only have a one-way causality pattern. The VAR estimation results show that weather elements (rainfall, temperature and duration of sunlight) do not significantly affect pneumonia in the short term. Meanwhile, the VECM estimation results show that in the long-term pattern, humidity variables affect pneumonia. For this reason, it is recommended that the relevant agencies carry out outreach to the public, especially to pneumonia sufferers, to avoid damp weather. Because the lower the humidity value, the greater the potential for pneumonia in Pangkalpinang City, Bangka Belitung Islands Province.

Projection generalized correntropy twin support vector regression

A projection generalized maximum correntropy twin support vector regression algorithm is proposed. The generalized correntropy function is added into the loss function of adaptive filtering, maximizing which can suppress the interference of noise or outliers.Considering the fact that single-shift projection twin support vector regression cannot observe local information of samples, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with wavelet soft threshold denoising is used to assign weights to samples. The CEEMDAN is used to decompose the original data, calculate the Pearson correlation coefficient between the mode functions and the original data. The mode with low correlation is filtered by wavelet based algorithm with soft-threshold to get the reconstructed samples after noise reduction. Smaller weights will be assigned to reconstructed samples with significant differences from the original data, while larger weights will be assigned to reconstructed samples with smaller differences. Similarly, the empirical risk term in the cost function is also assigned calculated weights to improve the robustness. Due to the use of empirical mode decomposition, the proposed method is particularly suitable for processing non-stationary data. Experimental results on artificial and UCI datasets verified the effectiveness of the algorithm.

Adaptive bagging-based dynamic ensemble selection in nonstationary environments

The classification of nonstationary data streams presents a substantial challenge mainly due to the simultaneous presence of class imbalance and concept drift. Ensemble learning is a widely acknowledged approach to address these two problems. Nevertheless, most of the existing methods establish independent strategies for each, ignoring their relationship and interactions, which prevents the corresponding strategies from performing as expected. This paper presents a novel algorithm AB-DES (Adaptive Bagging-based Dynamic Ensemble Selection), which employs bagging technique and dynamic ensemble selection system. In AB-DES, instances are stored to balance subsequent data chunks instead of relying solely on resampling methods. Then, all base classifiers are updated using a new dual adaptive sampling bagging strategy, which can help to reduce the classification inaccuracies involving by class imbalance. In the prediction phase, DES system constructs different ensemble models for different instances. The inclusion of a sliding window-based drift detector makes the above two strategies adapt to concept drift. Experimental results show that AB-DES achieves higher classification accuracy and exhibits enhanced robustness compared to 8 state-of-the-art methods on 10 synthetic datasets and 5 real world datasets with different types of concept drift.

Bin.INI: An ensemble approach for dynamic data streams

Class imbalance and concept drifts could deteriorate the performance of classifiers in data stream learning as their co-occurrence presents a complicated learning scenario. This situation becomes more complicated if missing values appear in the data. To tackle this challenge, this paper proposes a novel ensemble method, BINary classification in Incomplete, Non-stationary, and Imbalanced data streams (Bin.INI). As part of Bin.INI, Adapt2Regress builds a linear regression models pool using completely available minority subspaces, monitored under a novel sigmoid-decay function to provide imputation support in a drifting data environment. We use Min++ to balance the data in terms of imbalance complexity, a resampling component proposed earlier. Additionally, the 2-STage Ensemble Pruning technique (2STEP) is proposed which groups the classifiers based upon multiple diversity measures at the first step and selects the best subset using a novel custom metric, Sigmoid-Weighted Imbalance Score, at the second step. By using Adapt2Regress, Min++, and 2STEP, the proposed method can efficiently deal with incomplete, imbalanced, and drifting data in data streams. Experiments are conducted on 350 highly imbalanced data streams containing a variety of concept drifts. 300/350 data streams contain different ratios of missing data (5%–30%) in the minority space. Experiments reveal a noticeable decline in the performance of state-of-the-art approaches when faced with 15% or more missing data, while Bin.INI maintains its performance.

Conventional and unconventional shadow rates and the US state-level stock returns: Evidence from non-stationary heterogeneous panels

This study analyzes how monthly stock returns in the United States react to conventional and unconventional shadow rates from February 1994 to April 2023. The study uses a nonstationary heterogeneous panel data technique appropriate for analyzing large cross-sections and long periods. The analysis is separated into turbulent and tranquil periods. The findings suggest that, although the shadow rate is expected to align with the long-term rate, its ability to boost economic activity in the stock markets is only applicable in the short term. Despite the Federal Funds Rate (FFR) being unable to be lowered below zero bounds, the study shows results that support the effectiveness of the FFR in stimulating stock returns in the long run, particularly during crisis periods. The study also reveals that both conventional and unconventional shadow rates share a common feature, which is that they demonstrate how the stock markets can be downward-sticky in the long run with a rising shadow rate in virtually all 50 states in the U.S. The findings provide sturdy insights into the usefulness of unconventional monetary policy measures for stock market performance during crises and normal periods.

An experimental review of the ensemble-based data stream classification algorithms in non-stationary environments

Data streams are sequences of fast-growing and high-speed data points that typically suffer from the infinite length, large volume, and specifically unstable data distribution. Ensemble learning as a prevalent classification approach is widely used in data stream mining studies. Besides the impressive performance of ensemble learning algorithms in providing a collection of diverse and accurate classifiers, they are specifically efficient in handling non-stationary data streams. Due to the component-based nature and the chance of dynamic updates for the components of the ensemble, this category is appropriate for dynamically learning the changing concepts of the data. Several review research have been conducted on the challenges of non-stationary data streams so far. None of the available surveys are dedicated to examine the effect of ensemble learning models on concept drift handling. This paper aims to provide a thorough theoretical and experimental review of the most significant ensemble-based data stream classification approaches in confronting with various types of concept drifts. In this comprehensive experimental analysis, 21 state-of-the-art ensemble algorithms are tested on 30 synthetic datasets under various types and volumes of concept drifts. This experimental analysis involves analyzing the classification accuracy, kappa score and running time of the algorithms, along with performing various statistical tests. In addition to conducting experimental analysis, a comprehensive investigation is carried out on the most significant challenges posed by non-stationary data streams, which provides the reader with valuable insights.

Forecasting the Unemployment Rate in the Philippines using Box-Jenkins Methodology: Advancing Economic Strategies for Sustainable Development Goals

Unemployment remains a critical macroeconomic issue in Southeast Asia, significantly impacting the Philippines. This study aims to forecast the unemployment rate in the Philippines using ARIMA models to inform policy decisions and economic planning. The study utilized ARMA model for time series forecasting, leveraging past values and error terms to predict future data points. The methodology involved identifying suitable models through stationarity tests and autocorrelation analyses, estimating model parameters, and conducting diagnostic checks to ensure accuracy. Finally, the models were used to forecast future values and compute confidence intervals, providing insights into potential future trends. Monthly unemployment data from 2019 to 2023 was analyzed using the ARIMA (1,1,0) model, selected for its optimal fit based on statistical criteria. Initial findings indicated non-stationary unemployment data, necessitating first differencing to achieve stationarity. The ARIMA (1,1,0) model showed robustness, evidenced by diagnostic checks and Portmanteau test results confirming white noise residuals. Forecasts from 2024 to 2050 suggest fluctuating unemployment rates until 2034, followed by a gradual decline, indicating potential economic growth and improved labor market conditions. These forecasts align with the broader goal of sustainable economic development and highlight the importance of strategic policy interventions to foster job creation and economic stability in the Philippines. The study highlights the efficacy of ARIMA models in capturing unemployment trends and aiding policymakers in mitigating the socio-economic impacts of unemployment.

A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent, unpredictable attacks, affects individuals of all ages. IoT-based seizure monitoring can greatly enhance seizure patients' quality of life. IoT device acquires patient data and transmits it to a computer program so that doctors can examine it. Currently, doctors invest significant manual effort in inspecting Electroencephalograph (EEG) signals to identify seizure activity. However, EEG-based seizure detection algorithms face challenges in real-world scenarios due to non-stationary EEG data and variable seizure patterns among patients and recording sessions. Therefore, a sophisticated computer-based approach is necessary to analyze complex EEG records. In this work, the authors proposed a hybrid approach by combining traditional convolution neural (CN) and recurrent neural networks (RNN) along with an attention mechanism for the automatic recognition of epileptic seizures through EEG signal analysis. This attention mechanism focuses on significant subsets of EEG data for class recognition, resulting in improved model performance. The proposed methods are evaluated using a publicly available UCI epileptic seizure recognition dataset, which consists of five classes: four normal conditions and one abnormal seizure condition. Experimental results demonstrate that the suggested approach achieves an overall accuracy of 97.05% for the five-class EEG recognition data, with an accuracy of 99.52% for binary classification distinguishing seizure cases from normal instances. Furthermore, the proposed intelligent seizure recognition model is compatible with an IoMT (Internet of Medical Things) cloud-based smart healthcare framework.

Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting.

This article presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top of a generic forecasting model, including deep learning architectures. EnCQR exploits a bootstrap ensemble estimator, which enables the use of conformal predictors for time series by removing the requirement of data exchangeability. The ensemble learners are implemented as generic machine learning algorithms performing quantile regression (QR), which allow the length of the PIs to adapt to local variability in the data. In the experiments, we predict time series characterized by a different amount of heteroscedasticity. The results demonstrate that EnCQR outperforms models based only on QR or conformal prediction (CP), and it provides sharper, more informative, and valid PIs.

Time-frequency co-movements between commodities and global economic policy uncertainty across different crises

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. This comprehensive study examines the time and frequency spillovers between the Economic Policy Uncertainty [1] and a broad set of commodities encompassing ferrous, non-ferrous, and precious metals, food, and energy commodities over a period from December 1997 to April 2022, which includes various political, economic and health crises.The novelty of this research lies in its extensive temporal and categorical coverage, providing an understanding of how different types of commodities respond to various crises. Furthermore, our study breaks new ground by employing wavelet analysis to gain detailed insights in both time and frequency domains in the financial time series of interest, providing a deeper understanding of the co-movements and lead-lag relationships. Specifically, we introduce the Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analysis.Our findings demonstrate that not all crises uniformly impact commodities. Notably, during the global financial crisis and the COVID-19 pandemic, co-movements between commodities became significantly stronger. These results highlight the heterogeneity within the commodity asset class, where individual commodities exhibit diverse underlying dynamics. Importantly, the proposed methodology facilitates the extraction of robust results even when dealing with nonlinearities and nonstationary time series data. Consequently, our work offers valuable insights for policymakers (including regulatory bodies), investors, and fund managers.

Comparative Study on PSD and HHT Techniques for Condition Assessment of Steel Truss Bridge Using Acceleration Data

Bridge structures deteriorate over time due to factors like corrosion, fatigue, aging of materials, and unexpected natural or accidental events. Consequently, the vibrations measured in these structures exhibit time-varying and non-stationary characteristics. Additionally, non-linearities are inherent in these structures due to material behavior, boundary condition changes, and joints or connections. Analysis of non-linear and non-stationary structural vibration data is necessary to extract information on the condition of the bridge. Most methods assessing bridge conditions from acceleration responses rely on the Power Spectral Density (PSD) which is based on the Fourier Transform. However, the accuracy of the Fourier transforms for non-linear and non-stationary signals limits its application potential. The Hilbert–Huang Transform (HHT) has emerged as an alternative for handling these non-stationary signals due to its time-frequency-energy representation. This study provides a comparative study of both techniques by utilizing the data obtained from continuously monitored Pamban Bridge. This steel truss bridge was fitted with accelerometers to record bidirectional acceleration time histories at twenty bottom node points on the bridge. This study examines the continuous variations in the frequency component by both methods for 136 days. A linear best-fit trendline is obtained for the frequency parameters at different sensor positions. The slope and intercept values of this linear best fit trendline is further studied. The HHT showcases various evolving dominant frequency components over time in contrast to the PSD representation. The frequency spectrum from the HHT displays less variation over time at each sensor position compared to the predominant peak value derived from the PSD of the acceleration signal. The spatial variability in the predominant frequency obtained from HHT is also less than the PSD. Therefore, the ability of HHT to discriminate damages needs further investigations.

Continual pre-training mitigates forgetting in language and vision

Pre-trained models are commonly used in Continual Learning to initialize the model before training on the stream of non-stationary data. However, pre-training is rarely applied during Continual Learning. We investigate the characteristics of the Continual Pre-Training scenario, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks. We introduce an evaluation protocol for Continual Pre-Training which monitors forgetting against a Forgetting Control dataset not present in the continual stream. We disentangle the impact on forgetting of 3 main factors: the input modality (NLP, Vision), the architecture type (Transformer, ResNet) and the pre-training protocol (supervised, self-supervised). Moreover, we propose a Sample-Efficient Pre-training method (SEP) that speeds up the pre-training phase. We show that the pre-training protocol is the most important factor accounting for forgetting. Surprisingly, we discovered that self-supervised continual pre-training in both NLP and Vision is sufficient to mitigate forgetting without the use of any Continual Learning strategy. Other factors, like model depth, input modality and architecture type are not as crucial.

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

The consecutive submesoscale sea surface processes observed by an unmanned aerial vehicle (UAV) were used to decompose into spatial waves and current features. For the image decomposition, the Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) method was employed to disintegrate multicomponent signals identified in sea surface optical images into modulated signals characterized by their amplitudes and frequencies. These signals, referred to as Bidimensional Intrinsic Mode Functions (BIMFs), represent the inherent two-dimensional oscillatory patterns within sea surface optical data. The BIMFs, separated into seven modes and a residual component, were subsequently reconstructed based on the physical frequencies. A two-dimensional Fast Fourier Transform (2D FFT) for each high-frequency mode was used for surface wave analysis to illustrate the wave characteristics. Wavenumbers (Kx, Ky) ranging between 0.01–0.1 radm−1 and wave directions predominantly in the northeastward direction were identified from the spectral peak ranges. The Optical Flow (OF) algorithm was applied to the remaining consecutive low-frequency modes as the current signal under 0.1 Hz for surface current analysis and to estimate a current field with a 1 m spatial resolution. The accuracy of currents in the overall region was validated with in situ drifter measurements, showing an R-squared (R2) value of 0.80 and an average root-mean-square error (RMSE) of 0.03 ms−1. This study proposes a novel framework for analyzing individual sea surface dynamical processes acquired from high-resolution UAV imagery using a multidimensional signal decomposition method specialized in nonlinear and nonstationary data analysis.

Change-point analysis for binomial autoregressive model with application to price stability counts

The first-order binomial autoregressive (BAR(1)) model is the most frequently used tool to analyze the bounded count time series. The BAR(1) model is stationary and assumes process parameters to remain constant throughout the time period, which may be incompatible with the non-stationary real data, which indicates piecewise stationary characteristic. To better analyze the non-stationary bounded count time series, this article introduces the BAR(1) process with multiple change-points, which contains the BAR(1) model as a special case. Our primary goals are not only to detect the change-points, but also to give a solution to estimate the number and locations of the change-points. For this, the cumulative sum (CUSUM) test and minimum description length (MDL) principle are employed to deal with the testing and estimation problems. The proposed approaches are also applied to analysis of the Harmonized Index of Consumer Prices of the European Union.

Manifold embedding stationary subspace analysis for nonstationary process monitoring with industrial applications

Industrial processes frequently exhibit nonstationary characteristics due to factors like load fluctuations and external interference. Accurate monitoring of nonstationary industrial processes is of vital importance in ensuring production stability and safety. Unfortunately, most existing monitoring methods overlook the manifold structure presented in nonstationary data due to nonstationary features, causing the loss of critical information and poor interpretability. As a consequence, monitoring performance is compromised. To address this issue, this paper proposes a manifold embedding stationary subspace analysis (MESSA) algorithm. By embedding a neighborhood preservation term into the objective function of SSA, MESSA effectively mitigates the impact of nonstationarity on manifold structure. The extracted features incorporate both global stationarity and local manifold characteristics, facilitating a more comprehensive reconstruction of the intricate underlying mechanisms in industrial processes. This contributes to a substantial enhancement in the accuracy and reliability of process monitoring. A set of nonstationary swiss-roll dataset is designed to visually demonstrate the capability of MESSA in extracting manifold structure. Case studies including a numerical case, a continuous stirred tank reactor system and a real industrial roasting process validate the superior monitoring performance of the proposed method.

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R2 score. In our work, a MAPE value of 0.07772 and an R2 score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods.

A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data Decomposition

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), permutation entropy (PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine (RELM), multi-head attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components as follows: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high-, mid-, and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Beetle Optimizer (DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. The empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM, and MHA-BiGRU. In comparison with other benchmark models, the proposed model exhibits a reduction in Root Mean Squared Error (RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Detekcija klimatskih kolebanja Hilbert-Huangovom metodom na podacima stoljetnog opservatorija Zagreb-Grič, Hrvatska

Advanced statistical methods are applied on long-term data sets from Zagreb-Grič centennial observatory in Croatia to address recent climate change and variabilities; annual averages are used. Hilbert-Huang transform (HHT), consisting of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), is an empirically based data-analysis method for extracting periodic components embedded within generally nonlinear and non-stationary data. The EMD splits the original series into a so-called intrinsic mode functions (IMFs). First, using the EMD algorithm, intrinsic mode functions (IMFs) are obtained. The analysis of low frequency IMFs indicates significant influence of the North-Atlantic Oscillation on the air temperature, cloudiness and precipitation series in the part of northern Croatia. For the considered climatic elements, the analysis revealed mild deviations in natural fluctuations of the analyzed signal for the last 30 to 40 years, which are most likely caused by anthropogenic activities. Next, HSA is applied to each IMF obtained for the series of annual averages of sea level air pressure. In order to validate this approach and the results, an associated Hilbert spectrum (HS) is compared with the continuous wavelet analysis, i.e., this is another corresponding checkup. The comparison shows significantly improved time and frequency resolution in favor of HS. Moreover, HS provides a unique capability of displaying intra-wave frequency modulation, i.e., changes in frequency that occur within one cycle of oscillation.

Support vector machine-based stock market prediction using long short-term memory and convolutional neural network with aquila circle inspired optimization

ABSTRACT Predicting the stock market is one of the significant chores and has a successful prediction of stock rates, and it helps in making correct decisions. The prediction of the stock market is the main challenge due to blaring, chaotic data as well as non-stationary data. In this research, the support vector machine (SVM) is devised for performing an effective stock market prediction. At first, the input time series data is considered and the pre-processing of data is done by employing a standard scalar. Then, the time intrinsic features are extracted and the suitable features are selected in the feature selection stage by eliminating other features using recursive feature elimination. Afterwards, the Long Short-Term Memory (LSTM) based prediction is done, wherein LSTM is trained to employ Aquila circle-inspired optimization (ACIO) that is newly introduced by merging Aquila optimizer (AO) with circle-inspired optimization algorithm (CIOA). On the other hand, delay-based matrix formation is conducted by considering input time series data. After that, convolutional neural network (CNN)-based prediction is performed, where CNN is tuned by the same ACIO. Finally, stock market prediction is executed utilizing SVM by fusing the predicted outputs attained from LSTM-based prediction and CNN-based prediction. Furthermore, the SVM attains better outcomes of minimum mean absolute percentage error; (MAPE) and normalized root-mean-square error (RMSE) with values about 0.378 and 0.294.