Enhancing Predictive Business Analytics With Sarima-Based AI Algorithm

Abstract

In today’s business data analytics arena, which is known for its advanced technology, organizations that seek informed decision-making must integrate AI and achieve competitive advantage through predictive analytics. A predictive business analytics algorithm (SARIMA) and AI techniques, which are combined in this proposal, are introduced to enhance financial services. By integrating temporal patterns as portrayed by SARIMA and the stochastic nature of AI, we hope to build a robust forecasting tool that assists organizations in making evidence-based, informed decisions. This discussant makes a coherent description, reasons and support of an algorithm to be used, which is informed by academic and non-academic literature, with literacy material to follow.

Introduction

Emerging quickly on the business scene, Business Data Analytics recently has acquired an edge in terms of firms willing to comprehend future trends and base their choices on existing relevant information. The proposed algorithm represents a transformative convergence between SARIMA and AI using a hybrid approach of incorporating benefits from both techniques to ascend the level of predictability to never-before-seen heights. SARIMA models can be the essential source of statistical forecasting while assisting AI in learning through the adaptation and recognition of operational conditions, thus enabling businesses to proactively anticipate and respond quickly to changeable operating conditions of the environment. By adopting this cutting-edge proposition, business entities have specific opportunities to exploit the benefits of advancement in market complexity. At the same time, it can support companies to create an agile and resilient environment to face market challenges. This algorithm integrates the data-driven approach that has become a must component in modern-day business doings. Thus, this algorithm provides a powerful toolkit for future uncertainties.

Rationale

SARIMA

Seasonal Autoregressive Integrated Moving Averages (SARIMA), generally acknowledged and well-regarded, is a forecasting method that utilizes seasonal ARIMA to capture seasonal trends meaningfully. Recognized for its advanced functionality, SARIMA works well with hidden order sequences that usually surface in data; it is a performant tool for companies looking for an accurate picture of the time series component (Abellana et al., 2020). The proficiency of the model in cyclic fluctuations modifying forecasting accuracy is a determinant condition for its relevance in business applications, thus making it a valuable asset.

The AI technique of SARIMA is a crucial part of revealing the minor peculiarities in time sequence data. Hence, it enables the organizations to have insightful and successful determination. By overcoming the intricate problem of coping with multi-temporality, SARIAM generates additional explanatory power to predict future behaviour and delves into the hidden cyclic patterns of the data. This high level of complexity can add the apparatus for analyzing and forecasting the sporadic patterns to the SARIMA model toolbox, which makes it an irreplaceable expert in making predictions and analyses that are the primary requirements in various fields of businesses where correct forecasts and detection of chaos are the vital elements. Besides, this technique is well-known and widely used in industries like sales forecasting, where one of its functions is to detect and visualize cyclic patterns easily. Then, it is applied to demand prediction, which can handle recurrent variations over specific time intervals. Ultimately, it can plan and manage the inventory by accounting and having a good strategy to deal with the supply and demand volatility. 

The five numerical time series functions of SARIMA are the companies’ analytical solid tool for more accurate forecasting of the coming challenges, improving the decision-making process, and allocating resources appropriately. With the rising importance of data-driven insights among businesses, SARIMA, still a longstanding friend, gives a keen method of looking at valuable patterns in the series time data, no doubt the best in many of the operational contexts.

The SARIMA model is expressed mathematically as:

Y t =ϕ 1 Y t−1 +ϕ 2Y t−2+…+θ 1ϵ t−1 +θ2 ϵ t−2+…+ϵ t

It is the observed value at time t, ϕi are autoregressive parameters, ϵt is the error term, and θi are moving average parameters.

AI Integration

Including Artificial Intelligence (AI) in the SARIMA framework – a new scheme – is a significant improvement in creating a more responsive and adaptable model. Machine learning abstractions such as neural networks and deep learning are becoming a transforming factor because they are essentially algorithms capable of recognizing or analyzing complicated patterns and relationships from massive amounts of data (Farsi et al., 2021). As AI is injected into the system, SARIMA can better distinguish regular fluctuations from critical events. That is particularly noticeable when complex temporal dependencies and non-linear relationships exist. 

The AI system of this integrated model stands out as it has a learning ability and keeps its concept of a particular issue straight based on the new data it encounters. Adjustment to such changes represents one of the main strengths of an adaptive forecasting method; in market situations, where patterns constantly evolve and emerging trends call for a prompt response, such services become indispensable—integrating SARIMA and AI’s power allows organizations to build a robust predictive model focusing on contemporary data environments with a higher probability of accuracy and agility. 

Literature Review

SARIMA in Business Analytics

The critical scholar literature, represented by the groundbreaking work of Box and Jenkins on time series, accentuates the significant role played by the Seasonal Autoregressive Integrated Moving Average (SARIMA) in shining the light on essential some parts of business data. Studies across a broad spectrum of business environments have repeatedly shown that SARIMA mechanisms are effective and have diverse applications. They include predicting future outcomes accurately because they can capture complex and dynamic relationships. Consequently, SARIMA has developed as an advanced tool in modern-day analytics, tested in empirical research and widely accepted by the scholarly community as a valid method for predicting hidden and complicated business cycles.

AI in time series forecasting

This study by Hyndman and Athanasopoulos proves that machine learning, like AI (Artificial Intelligence), is essential in improving model accuracy of time series forecasting. Their works show the power of neural networks and how machine-learning algorithms with this subset function produce excellent results. The brain of neural networks is adroit in revealing the intrinsic underlying patterns and interrelations, which are hard to discover by conventional approaches, resulting in more accurate predictions when dealing with matters of high dynamics and complexity. This predominant data mining approach utilizes cututilizese computational methods such as neural networks. It is a tribute of sorts to a new era of time series analysis in the company of intelligent automated systems that are capable of uncovering the sophistry of temporal trends and refining the accuracy of the forecasting, therefore putting the predictive modelling in the frontier of diverse fields like finance, weather and so on in use.

Proposed SARIMA-based AI Algorithm

This algorithm realizes a realized union between the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a neural network that contributes to high forecasting accuracy levels. These components merged perfectly as the model virtually picked up these recurrent patterns from the time series data, providing a concise yet comprehensive model for forecasting. The shared responsibility is the simultaneous part where the neural network component shows off as being efficient in handling and adjusting, at the same time, subtly non-linear relationships within the dataset.

 By pairing these approaches, the algorithm harnesses the features of both SARIMA’s proficiency in seasonality and trends with algorithms to match the neural network’s ability to cope with complex relationships. We get an innovative forecasting solution that outsmarts the existing ones as it is designed to effectively handle the vast amount of information relevant to dynamic and complex temporal scenarios (Koyuncu et al., 2021). This proves the flexibility of the hybrid models and the positive effect they bring to enhance the accuracy of prediction. The algorithm is expressed as follows:

Yt=ϕ1Yt−1+ϕ2Yt−2+…+θ1ϵt−1+θ2ϵt−2+…+ϵt +f (AI Model)

Where f (AI Model) represents the output of the neural network.

Illustrative elements

SARIMA parameters

Time series data

Source: https://towardsdatascience.com/time-series-forecasting-with-sarima-in-python-cda5b793977b?gi=3c9a0fb79e9c

Neural Network Architecture

Source: https://dataaspirant.com/neural-network-basics/

Conclusion

To sum up, the application of SARIMA and AI in predictive business analytics is a compelling strategy for decision-making purposes, especially for enterprises needing more precise forecasting tools that would do well to use seasonal autoregressive integrated moving averages and artificial intelligence-based solutions. Such a synergy brings vitality to SARIMA with the ability to capture intricate temporal patterns and to AI, which is flexible in adapting to rapidly changing environments; this synergy offers a holistic solution. Beyond the boundaries of conventional prediction techniques, their amalgamation yields a forecasting system that enables the companies to overcome uncertainties and obtain a competitive advantage.

Education on the rationale and the proposed solution aspects that reinforce the use of SARIMA in time series analysis and the capabilities of AI, which excel in accurate predictions, are based on academic and non-academic literature. It is seen from the source material, especially the historical contribution by Box and Jenkins (1970) in the time series forecasting, that SARIMA is equally essential to AI. These two are virtually inseparable factors in the current issue.

Though the presentation will be enriched with the help of illustrative items, such as tables with SARIMA parameters and graphical depictions of time series, the proposal also promotes the integration of SARIMA and AI, in addition to outlining the steps needed to build and implement the proposed model. Combining the conceptual underpinning that is a major driver in this predictive business analytics and, on the other hand, the practical applications to precision is the new innovative era in how industries will thrift the future trends in this forever changing business environment.

References

Abellana, D. P. M., Rivero, D. M. C., Aparente, Ma. E., & Rivero, A. (2020). Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario. Journal of Tourism Futures, ahead-of-print(ahead-of-print). https://doi.org/10.1108/jtf-07-2019-0070

Farsi, M., Hosahalli, D., Manjunatha, B. R., Gad, I., Atlam, E.-S., Ahmed, A., Elmarhomy, G., Elmarhoumy, M., & Ghoneim, O. A. (2021). Parallel genetic algorithms optimize the model for better forecasting of the NCDC weather data. Alexandria Engineering Journal, 60(1), 1299–1316. https://doi.org/10.1016/j.aej.2020.10.052

Koyuncu, K., Tavacioğlu, L., Gökmen, N., & Arican, U. Ç. (2021). Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models. Maritime Policy & Management, 1–13. https://doi.org/10.1080/03088839.2021.1876937