skillascend

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​Accurate traffic forecasting is crucial for efficient telecommunications network management. Various models are employed to predict network traffic patterns, each with distinct advantages:​
1. Statistical Models
Seasonal Autoregressive Integrated Moving Average (SARIMA): Extends ARIMA by incorporating seasonal elements, effectively modeling recurring patterns in network traffic.​
Holt-Winters Exponential Smoothing: Captures level, trend, and seasonal components through exponential smoothing, suitable for data with seasonal variations.​
2. Machine Learning Models
Random Forests and Gradient Boosting Machines: Ensemble methods that combine multiple decision trees to enhance predictive accuracy, adept at modeling complex nonlinear relationships.​
Support Vector Machines (SVMs): Supervised learning models used for classification and regression, effective in capturing intricate patterns within network traffic data.​
3. Deep Learning Models
Recurrent Neural Networks (RNNs): Designed to recognize patterns in sequential data, capturing temporal dependencies in network traffic.​
Long Short-Term Memory (LSTM) Networks: A type of RNN capable of learning long-term dependencies, effectively modeling both short-term fluctuations and long-term trends.​
Graph Convolutional Networks (GCNs): Operate on graph-structured data, capturing spatial dependencies between nodes, useful in modeling interactions among network components.​
4. Hybrid Models
Spatio-Temporal Hybrid Graph Convolutional Networks (STHGCN): Combine GCNs with temporal modeling techniques to capture both spatial and temporal dependencies, enhancing forecasting accuracy.​
5. Federated Learning Models
Federated Learning Approaches: Enable decentralized data processing across multiple devices, preserving data privacy while facilitating collaborative learning for traffic forecasting.​
Each model offers unique strengths, and selection depends on specific network requirements and data characteristics.

SkillAscend.com

Our model for causal inference are capable to -Learning causal structure from the data -Moderating the learned causal structure with domain knowledge -Causal Influence Score -Causal Influence Visualization

For a visual explanation of fault impact analysis using AI/ML in 5G networks, you may find the following video informative:
<a href="https://youtu.be/mn3UM06oqVY" target="_blank" rel="noopener">Fault impact analysis for 5G using AI/ML</a>
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Machine learning to determine abnormal KPI and dynamically configure KPI threshold. a.Analyses multiple KPIs from different sources after each KPI interval b.Handles Time Series Data c.Takes into account relationship between KPIs d.Dynamic Thresholding e.KPI instance is flagged as abnormal, on threshold breach f.Set ground truth, against each prediction g.Uses KPI anomalies as an event in Fault Assessment

Machine Learning based Fault and Performance Management of Telecommunication Networks
SkillAscend is a Hyderabad-based company specializing in telecommunication services, learning management services and consulting solutions. Founded in 2020, the company focuses on applying advanced technologies in telecommunications Core, OSS and BSS systems and applications. ​
SkillAscend offers consulting solutions in machine learning-based fault and performance management for telecommunication networks. Their services include fault prediction, abnormal KPI detection, causal reasoning, and traffic and performance forecasting, tailored to various network technologies such as UMTS, LTE, 5G, broadband networks, and transport networks like optical and MPLS.
SkillAscend maintains an active online presence through various platforms. Their YouTube channel provides tutorials on essential skills in&nbsp; Data Analytics, AI, and general tech skills. &nbsp;​
The company is led by professionals with extensive experience in various telecommunications job sectors.&nbsp;
According to Glassdoor reviews, employees have reported valuable learning experiences working on data science problems related to the telecom industry. ​
<a href="https://www.skillascend.com">www.skillascend.com</a>
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