Predictive Analytics and Time Series Forecasting

The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting of time series with a low signal to noise ratio, with a potential to scale adequately in a multi-cluster system due to the parallelized nature of the algorithm.

In this paper, based on the characteristics of real-time and time characteristics of financial data, time series is used as an entry point to mine financial data. Using the trigonometric polynomial graph method, the visualization of financial and economic cube data and the definition of clustering distance are carried out. Combined with wavelet analysis theory to decompose the periodic, trend and random terms of financial data time series, so as to predict the development of financial data with different scale components. In addition, BIRCH method is utilized to construct CF tree through data clustering features for micro-clustering of financial data. And K-meadiods method is chosen to fill the gap of BIRCH method in the application of large datasets to realize the macro-clustering of financial and economic data, and to establish BIRCH.K-meadiods clustering method. Compared with similar models, the prediction and clustering model of financial data based on time series has an overlap rate of >99.00% between the predicted and actual values of financial data and an error in the interval of (0,300), which can assist in the in-depth mining and clustering analysis of the value of financial data with high-precision prediction, and expand the level of decision-making considerations for the internal management of the enterprise and even for the external construction.

This paper explores innovative approaches that combine financial risk management with time series forecasting. Financial risk management is particularly crucial in highly volatile and complex financial markets, as a core mechanism for financial institutions and investors to mitigate potential losses. Time series forecasting, a mathematical and statistical tool, offers distinct advantages in identifying and analyzing variations in data over time. This paper reviews the theoretical foundation of time series forecasting in financial risk management, analyzing its fundamental principles and applicability in financial risk assessment. The key technique and methods are discussed, including model selection and optimization, interactive analysis of time series data, and integrated risk control strategies. An innovative risk management model based on time series analysis is proposed, and its theoretical foundation and applicability are elaborated upon, providing a new theoretical framework and methodological support for financial risk management.

The purpose of this research is to evaluate tax compliance based on the behavior of taxpayers' financial and non-financial data. This research is expected to be useful as material for evaluating the level of tax compliance of taxpayers. This study uses a case study method (single case) and uses a mixed method. Descriptive analysis is used to analyze data from the research object in the form of a single case unit analysis. The results of this study indicate the perceived behavior control of taxpayers that shows low tax compliance behavior. The combination of analysis of financial and non-financial data from taxpayers produces a useful evaluation in determining the level of tax compliance. Based on this, the low achievement of tax revenue realization and high tax audit performance is directly influenced by the low tax compliance behavior of taxpayers.
 Keywords: Tax Compliance; Behavior; Financial Data; Non-Financial Data.

A dual-stage advanced deep learning algorithm for long-term and long-sequence prediction for multivariate financial time series

Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This paper proposes a novel multivariate hybrid neuro-fuzzy model for forecasting tasks, which is based on and generalizes the neuro-fuzzy model with consequent layer multi-variable Gaussian units and its learning algorithm. The model is distinguished by a separate consequent block for each output, which is tuned with respect to the its output error only, but benefits from extracting additional information by processing the whole input vector including lag values of other variables. Numerical experiments show better accuracy and computational performance results than competing models and separate neuro-fuzzy models for each output, and thus an ability to implicitly handle complex cross correlation dependencies between variables.

Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain-specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice-enabled devices. This work presents a novel system that enables both experts in the finance domain and non-expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG). The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.

Poly-linear regression with augmented long short term memory neural network: Predicting time series data

With the advances in information science, vast amounts of financial time series data can been collected and analyzed. In modern time series analysis, sequential pattern mining (SPM) and association discovery (AD) are the most important techniques to predict the future trends. This study aims at developing advanced SPM and AD for financial data by cutting edge techniques from artificial intelligence and machine learning. The nonlinearity and non-stationarity of financial time series dynamics pose a major challenge for SPM and AD. This study employs time–frequency analysis to extract features for SPM. Then, a sparse multi-manifold clustering (SMMC) is used to partition the feature space into several disjointed regions for better AD. Finally, local relevance vector machines (RVMs) are employed for AD and perform the forecasting. Different from traditional methods, the novel forecasting system operates on multiple resolutions and multiple dynamic regimes. SMMC finds both the neighbors and the weights automatically by a sparse solution, which approximately spans a low-dimensional affine subspace at that point. RVM, the Bayesian kernel machines, can produce parsimonious models with excellent generalization properties. Taking multiple time series data from financial markets as an example, the empirical results demonstrate that the proposed model outperforms traditional models and significantly reduces the forecasting errors. The framework is effective and suitable for other time series forecasting.

A novel featurization methodology using JaGen algorithm for time series forecasting with deep learning techniques

In building a decision support system (DSS), an important component is the modeling of each potential alternative action to predict its consequence. Decision makers and automated decision systems (i.e., modelbased DSSs) depend upon quality forecasts to assist in the decision process. The more accurate the forecast, the better the DSS is at helping the decision maker to select the best solution. Forecasting is an important contributor to quality decision making, in both the business world and for engineering problems. Retail stores and wholesale distributors must predict sales in order to know how much inventory to have on hand. Too little can cause lost sales and customer dissatisfaction—If too much is on hand, then other inventory problems can occur (i.e., cash flow, ad valorem tax, etc.). If the goods are perishable, it could most certainly be a financial loss. Items that occur over time, as in the number of cars sold per day, the position of an airplane, or the price of a certain stock are called “time series.” When these values are forecast, the accuracy can vary, depending on the data set and the method. This subject has been greatly discussed in the literature and many methods have been presented. Artificial neural networks (ANN) have been shown to be very effective at prediction. Time series forecasting is based upon the assumption that the underlying causal factors are reflected in the lagged data values. Many times, a complete set of the causal factors either is not known or is not available. Predictions are made based upon the theory that whatever has occurred in the near past will continue into the near future. Time series forecasting uses past values to try and predict the future. A slight modification to this concept is the application of recency. What happened more recently is closer to the current situation than the more distant past. The older data still contain knowledge, it just is not as important (or as correct) as the newest information. Things change, life is dynamic, and what used to be may be no more or may be to a different extent. Modification of the training algorithm of a neural network forecaster to consider recency has been proven on real economic data sets to reduce residual by as much as 50%, thereby creating a more accurate model which would allow for better decision making.

Group Decision Support Systems (GDSS) are defined and discussed. A GDSS model developed by the author is reviewed in depth for communication of the concepts of GDSS. The model's components are related to health care applications. Questions about unique requirements and level of sophistication in health care applications are explored. What are the differences? What is needed in GDSS software? How do implementation strategies differ? The purpose of this paper is to define and discuss the uniqueness and level of sophistication of GDSS applications in health care. The information requirements and level of information abstraction are the major forces considered in the design of specific medical GDSSs. Data for the GDSS and queries originate both internally and externally to the system. Raw data may be in image form and require extensive analysis by the decision makers for information to be extracted from the raw data. Efforts also are made to relate financial and medical data for better business decisions. This integration often has limited success. Additionally, financial data represent multiple sources and present concerns of validity and reliability. In medical diagnoses the knowledge bases are large and contain thousands of rules. Treatment planning and progress reporting rely on medical records that contain thousands of information items and that often require interpretation by an expert. These information attributes go beyond qualitative versus quantitative definitions and are the author's basis for the analysis presented in this paper.

Predictive analytics has become a crucial tool in modern business decision-making, particularly in demand forecasting. By leveraging historical data and statistical modeling techniques, businesses can enhance resource allocation, optimize inventory management, and improve overall operational efficiency. Among various predictive analytics methods, time series models are widely employed due to their ability to capture trends, seasonality, and patterns in demand fluctuations. This explores the application of time series models, including Moving Average (MA), Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (Holt-Winters method), and advanced deep learning models such as Long Short-Term Memory (LSTM) networks. These models enable businesses to make data-driven decisions by accurately predicting future demand, thereby reducing costs and improving supply chain responsiveness. Furthermore, the integration of predictive analytics in business resource allocation extends beyond inventory management. It influences workforce planning, financial forecasting, and supply chain optimization, ensuring that organizations align their operations with anticipated market trends. However, despite the benefits, challenges such as data quality issues, model interpretability, external disruptions, and scalability concerns remain key obstacles in implementing time series forecasting. Emerging advancements in artificial intelligence (AI), big data analytics, and cloud-based forecasting solutions are shaping the future of predictive analytics. The integration of real-time data and machine learning models offers new opportunities for businesses to enhance forecasting accuracy and agility. As demand forecasting continues to evolve, organizations must adopt a strategic approach to model selection and implementation to fully capitalize on the advantages of predictive analytics. This study provides a comprehensive overview of predictive analytics for demand forecasting, highlighting the role of time series models in optimizing business resource allocation and discussing future trends that will drive further innovation in this field.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

This study aims to explore the influence of decision-making, financial data analysis, and human resource training on company performance, as well as the role of artificial intelligence (AI) moderation in these relationships. Using the path analysis method with AMOS software, data was collected from 100 employees in a Telecommunications company in Indonesia. The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence. Additionally, AI serves as a significant moderator in the relationship between decision-making and company performance, but not in the relationship between financial data analysis and performance. These findings emphasize the importance of good decision-making and financial data analysis in improving organizational performance, and demonstrate the potential of AI in amplifying the positive impact of decision-making. This study has limitations in sample size and context, which need to be considered for further research.