Data Drift refers to the phenomenon where the generating model behind the data changes over time. Due to data drift, any model built on the past training data becomes less relevant and inaccurate over time. Thus, detecting and controlling for data drift is critical in machine learning models. Hierarchical Temporal Memory (HTM) is a machine learning model developed by Jeff Hawkins, inspired by how the human brain processes information. It is a biologically inspired model of memory similar in structure to the neocortex and whose performance is claimed to be comparable to state of the art models in detecting anomalies in time series data. Another unique benefit of HTMs is their independence from training and testing cycles; all the learning takes place online with streaming data, and no separate training and testing cycle is required. In the sequential learning paradigm, the Sequential Probability Ratio Test (SPRT) offers unique benefits for online learning and inference. This paper proposes a novel hybrid framework combining HTM and SPRT for real-time data drift detection and anomaly identification. Unlike existing data drift methods, our approach eliminates frequent retraining and ensures low false positive rates. HTMs currently work with one dimensional or univariate data. In a second study, we also propose an application of HTM in a multidimensional supervised scenario for anomaly detection by combining the outputs of multiple HTM columns, one for each data dimension, through a neural network. Experimental evaluations demonstrate that the proposed method outperforms conventional drift detection techniques like the Kolmogorov-Smirnov (KS) test, Wasserstein distance, and Population Stability Index (PSI) in terms of accuracy, adaptability, and computational efficiency. Our experiments also provide insights into optimizing hyperparameters for real-time deployment in domains such as Telecom. International Journal of Mathematical, Engineering and Management Sciences Vol. 10, No. 3, 777-796, 2025 https://doi.org/10.33889/IJMEMS.2025.10.3.039 777 | https://www.ijmems.in A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using E-mail: sujoy.roychowdhury@ericsson.com (Received on July 15, 2024; Revised on February 5, 2025 & February 19, 2025; Accepted on February 25, 2025) Abstract Data Drift refers to the phenomenon where the generating model behind the data changes over time. Due to data drift, any model built on the past training data becomes less relevant and inaccurate over time. Thus, detecting and controlling for data drift is critical in machine learning models. Hierarchical Temporal Memory (HTM) is a machine learning model developed by Jeff Hawkins , inspired by how the human brain processes information. Our experiments also provide insights into optimizing hyperparameters for real -time deployment in domains such as Telecom. Keywords- Hierarchical temporal memory (HTM), Sequential probability ratio test (SPRT), Time series, Real-time anomaly detection, Data drift detection, Streaming data analysis, Hybrid machine learning models , Telecom network monitoring, AI powered data drift detection. 1. Introduction Data drift is a phenomenon where the generating model of some data changes over time . In presence of data drift, any model trained on older data becomes less relevant, and its performance degrades with respect to the recently available data. Some examples of applications where data drift or anomaly detection is useful include fraud detection in banking and finance by detecting anomalies in transactions, anomaly detection in the healthcare industry by identifying anomalies in patients’ vital signs, Internet of Things (IoT) based sensor anomaly detection, and network intrusion detection in cybersecurity. Hierarchical Temporal Memory (HTM) (Hawkins et al., 2019; Numenta, 2019a) is a neural network model that is more biologically plausible than deep learning. It is an unsupervised method with no separation between training and testing phases. Consequently, it performs well with streaming data. 2.1 Hierarchical Temporal Memory (HTM) Hierarchical temporal memory was first introduced by Jeff Hawkins, based on the groundwork in the book “On intelligence” and further developed through Numenta’s research efforts (Hawkins et al., 2019 ; Numenta, 2019 a). HTMs are biologically inspired memory models that mimic the structure of the neocortex, functioning as associative memory neural networks . They are considered more biologically plausible than the traditional deep learning models such as Convolutional Neural Networks (CNNs). HTM’s architecture closely resembles the cortical columns of the neocortex of the mammalian brain. Acknowledgments This research did not receive any specific grant from funding agencies in the public, commercial, or not -for-profit sectors. The authors would like to thank the editor and anonymous reviewers for their comments that helped to improve the quality of this work. References Ahmad, S., & Purdy, S. (2016). Real -time anomaly detection for streaming analytics. arXiv Preprint , arXiv:1607.02480. Anandharaj, A., & Sivakumar, P.B. (2019). Anomaly detection in time series data using hierarchical temporal memory model. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (pp. 1287-1292). IEEE. Coimbatore, India. Kadam, S.V. (2019). A survey on classification of concept drift with stream data. HAL Archives . https://hal.science/hal-02062610/file/A_survey_on_classification_of_concept_drift_with_stream_data.pdf . Marshall, A.W., & Olkin, I. (1960). Multivariate Chebyshev inequalities. The Annals of Mathematical Statistics, 31(4), 1001-1014. Numenta (2019a). HTM white paper . Retrieved from https://numenta.com/neuroscience -research/research- publications/papers/hierarchical-temporal-memory-white-paper/. Numenta (2019b). The science of anomaly detection: How HTM enables anomaly detection in streaming data .