Analysis of Traffic on Telecommunication Network Using Time Series Forecasting Tool

Telecom companies are facing a lot of challenges and hindrances these days including network loads, connection bandwidth distribution, and infrastructure optimization. However, time series forecasting enables them to effectively address subscriber demands and auxiliary telecom services.

As the traffic demand for telecommunication networks increases companies are in struggle to furnish the best services to retain their subscribers. So, what’s the solution to this problem? Evidently, the introduction of Artificial Intelligence and Machine Learning to regularize fluctuating parameters. Further, time series forecasting is great technology today that is aiming to assist businesses with a prediction on time series data.

In this blog, we will discuss the benefits of analyzing traffic on a telecom network by using a time series forecasting tool– that does not require technical staff to operate. Further, we will also elaborate on how a time series forecasting tool can ease telecom analytics.

Analysis of Traffic on Telecom Network: A Novel Approach

Traffic load identification on a telecom network or channel is exigent these days for stakeholders. It’s because of an increasing number of smartphone devices, gadgets, and the adoption of digital transformation. The sustenance of this pace required a long-term strategy that will forecast the upcoming demands and innovate some techniques of traffic load balancing.

The flagship telecommunication companies in the United States including AT&T, Verizon, Comcast, and T-Mobile are in a battle to deliver the quickest internet, call, and mobile services. To adopt this revolution designing a network forecasting is a novel approach. Thus, time series forecasting is a highly effective strategy that achieves success through deep analysis of subscriber traffic.

Time Series Forecasting for Telecom Companies

Time-series forecasting is a technique that utilizes historical and current data to predict future values over a period of time or a specific point in the future. By analyzing data that we stored in the past, we can make informed decisions that can guide our business strategy and help us understand future trends. This time-labeled data is injected into a machine learning model or time series forecasting tool that easily tweaks data into significant insights. In traffic analysis, forecasting helps off with future traffic trend prediction and decision-making.

For example, a potential telecommunication company wants to predict its subscriber base for the upcoming year and the required network bandwidth. Then, time series forecasting will be a great strategy to find out and respond to them promptly.

In addition, using a time series and forecasting tool companies can transform their raw data into visual insights like BI (business intelligence) dashboards within minutes. Forecasting using a time series forecasting tool enabled telecom companies with innumerable benefits.

Benefits of Time Series Forecasting to Telecom Companies

Time series forecasting is essential for telecom companies because it facilitates better planning of resources and capabilities. The statistical analysis of subscriber traffic data prepares companies for what lies ahead for their business success. Enabling managers with valuable insights so that they can plan in a better way. Here I have some use cases and benefits of telecom that can be furnished with.

Traffic analysis of subscriber data to draw insights
Bandwidth distribution on a network
Optimization of infrastructure
Estimating telecommunication budgets
Designing targeted subscriptions
Governing the cost of Connect/Reconnect

Traffic Analysis of Telecom Data Using Time Series Forecasting Tool

If we talk about voice traffic analysis of telecom data on a 2G/3G channel. Time series analytics models like ARIMA are used to predict patterns within voice calls. In forecasting, different patterns in historical telecom data are analyzed pivoting on data seasonality and variations. It enables business executives to anticipate future outcomes in a better way.

For instance, if we plot a historical analysis of telecom data. The structural breaks within the sequence imply that there must be an external event that has happened. Thus, this analysis helps telecoms to make decisions more precisely.

Assume a scenario, you’re a potential telecom executive looking for a technique to optimize connect and reconnect in a digital circuit without scaling your infrastructure. You should use time series forecasting (anticipate upcoming events) which can answer how to host millions of calls in a single virtual circuit without scaling your current telecom infrastructure.

Odyx yHat: A Time Series Forecasting Tool For Everyone

Odyx yHat is a self-servicing time series forecasting tool designed to analyze data within minutes. It does not require any technical background to operate because its interface is specially designed keeping the non-technicals in mind.

You just have to select an industry & its sub-industry, upload data and we will run the best time series models for your data. Besides, we also facilitate companies to get daily updates from productionized (already in production) models. Imagine! It is that quick to conduct forecasting in our AI-Based tool.

The mission to design this tool is to make tomorrow’s technology available today. Enable stakeholders and decision-makers to draw insights within minutes. In the telecom sector, Odyx yHat has the ability to forecast time series data within minutes to deliver the above quoted benefits.

The Final Verdict

This blog elaborated on time series forecasting– a potential area of machine learning using a time series forecasting tool called Odyx yHat. This self-servicing tool helps telecom companies to tackle current challenges in the telecom business due to an increase in the usage of smartphone devices. Time series forecasting assists telecommunications with real-time traffic analysis, customers churn prediction, optimization of infrastructure, designing targeted subscriptions, and much more.

Odyx yHat is a time series forecasting tool designed for non-technical staff without any data science or machine learning knowledge. We design this tool to innovate the world by thinking beyond tomorrow. Let’s optimize your telecom business with powerful telecommunications time series forecasting. Book a free demo now!

Frequently Asked Questions

How does traffic analysis work?

Traffic analysis on a telecom network can be done through a time series forecasting tool that transforms telecom data into graphs and charts for better intuition for executives. Traffic analysis starts by comparing historical data with current data using statistical and machine learning techniques.

What is the analysis of patterns in telecoms?

The analysis of patterns means the shape or behavior of data. In other words, machine learning techniques are employed to detect patterns in data and draw the inner details of available data resources.

How can a time series forecasting tool help in telecommunication?

In telecommunications, time series forecasting solves multiple problems for telecom enterprises to scale their business, forecast upcoming demands, and meet their future needs to keep subscribers engaged.

Why is traffic analysis essential for telecom companies?

Traffic analysis will help companies to predict their future decisions more efficiently. So, that’s why traffic analysis is essential for telecom companies.