ARIMA Time Series Model for Telecommunication KPI Forecasting
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Solution Overview
Problem
Telecommunication networks face challenges in isolating and predicting Key Performance Indicators (KPIs) due to aggregated views and lack of standard techniques for complete KPI assessment, making it difficult to identify root causes of failures and forecast network impacts.
Innovation Solution
A Time Series based prediction/forecast model is developed using historical data from Performance Management counters and KPIs, pre-processed with machine learning and statistical techniques to evaluate stationarity, and built using ARIMA models with autocorrelation and partial autocorrelation procedures to identify modeling parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If aggregated views of PM counters and KPIs are used, then system-wide performance monitoring is achieved, but root cause identification and causal relationships between KPIs become difficult to detect
Solution Approach 1:
The patent segments the aggregated KPI data into individual time series components, allowing analysis of each KPI's temporal behavior separately. This segmentation enables detection of causal relationships and root causes while maintaining the comprehensive coverage of aggregated monitoring.
Solution Approach 2:
The patent introduces a time dimension by applying time series analysis to KPI data. By examining KPIs across multiple time points rather than as static aggregated values, the system can detect causal relationships and root causes through temporal patterns, autocorrelation, and forecasting.
2Device complexity
If standard techniques for complete KPI assessment are not available, then system complexity is reduced, but prediction accuracy and forecasting capability deteriorate
Solution Approach 1:
The patent changes the parameters of KPI assessment by applying statistical time series analysis methods (ARIMA modeling, autocorrelation analysis) to transform standard performance data into predictive insights. This enables accurate forecasting while maintaining manageable system complexity through established statistical techniques.
3Ease of operation
If reactive troubleshooting based on aggregated KPI views is used, then operational complexity is minimized, but response time and corrective action efficiency increase
Solution Approach 1:
The patent implements preliminary action by using time series forecasting to predict future KPI values and potential failures before they occur. This allows proactive identification of root causes and preventive corrective actions, reducing both operational complexity and response time by addressing issues before they impact service.
Data Source
AI summary
The present disclosure relates to system(s) and method(s) for building an ARIMA based Time Series prediction/forecast model for Key Performance Indicators (KPIs) and Performance Management (PM) counters in a telecommunication network. The system receives historical data, for a predefined period, associated with a prediction/forecast model. The system further pre-processes the historical data in order to evaluate statistical characteristics of stationarity of the historical data. Based on the evaluation, the system stationarizes the data first by backfilling anomalies and missing data and then applying techniques associated with differencing, moving averages and auto-correlation. The system further builds the Time Series based prediction/forecast model using the data using ACF and PACF correlation functions.