This dataset contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2022-06-30. This is an extended version of the dataset that was used in the Kaggle Wikipedia Web Traffic forecasting competition. For consistency, the same Wikipedia pages that were used in the competition have been used in this dataset as well. The colons (:) in article names have been replaced by dashes (-) to make the .tsf file readable using our data loaders.

The data were downloaded from the Wikimedia REST API. According to the conditions of the API, this dataset is licensed under CC-BY-SA 3.0 and GFDL licenses.

This is the aggregated version of the daily dataset used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-05, after aggregating them into weekly.

The original dataset contains missing values. They have been simply replaced by zeros before aggregation.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

In 2021, Chewy was the direct-to-consumer (D2C) brand with most online traffic, hitting *** million visits worldwide. Opensea ranked second with *** million online visits, followed by Fitbit at *** million visits.

Zalando, one of the biggest European e-commerce breakthroughs, logged 14 million website visits in December 2023. Over the six month period from July 2023 to December 2023, the German multi-national e-commerce company recorded its highest number of website visits in November 2023, with 15.5 million hits.

This file contains tab delimited data that looks something like the following: Hour Number of unique hits.Numpy has a utility function, genfromtxt(), that can be used to load in arbitrary text data, which is converted into a numpy array.

In March 2024, Amazon.com had approximately 2.2 billion combined web visits, up from 2.1 billion visits in February. In the fourth quarter of 2024, Amazon’s net income amounted to approximately 20 billion U.S. dollars. Online retail in the United States Online retail in the United States is constantly growing. In the third quarter of 2023, e-commerce sales accounted for 15.6 percent of retail sales in the United States. During that quarter, U.S. retail e-commerce sales amounted to over 284 billion U.S. dollars. Amazon is the leading online store in the country, in terms of e-commerce net sales. Amazon.com generated around 130 billion U.S. dollars in online sales in 2022. Walmart ranked as the second-biggest online store, with revenues of 52 billion U.S. dollars. The king of Black Friday In 2023, Amazon ranked as U.S. shoppers' favorite place to go shopping during Black Friday, even surpassing in-store purchasing. Nearly six out of ten consumers chose Amazon as the number one place to go find the best Black Friday deals. Similar findings can be observed in the United Kingdom (UK), where Amazon is also ranked as the preferred Black Friday destination.

Data dictionary: Page_Title: Title of webpage used for pages of the website www.cityofrochester.gov Pageviews: Total number of pages viewed over the course of the calendar year listed in the year column. Repeated views of a single page are counted. Unique_Pageviews: Unique Pageviews - The number of sessions during which a specified page was viewed at least once. A unique pageview is counted for each URL and page title combination. Avg_Time: Average amount of time users spent looking at a specified page or screen. Entrances: The number of times visitors entered the website through a specified page.Bounce_Rate: " A bounce is a single-page session on your site. In Google Analytics, a bounce is calculated specifically as a session that triggers only a single request to the Google Analytics server, such as when a user opens a single page on your site and then exits without triggering any other requests to the Google Analytics server during that session. Bounce rate is single-page sessions on a page divided by all sessions that started with that page, or the percentage of all sessions on your site in which users viewed only a single page and triggered only a single request to the Google Analytics server. These single-page sessions have a session duration of 0 seconds since there are no subsequent hits after the first one that would let Google Analytics calculate the length of the session. "Exit_Rate: The number of exits from a page divided by the number of pageviews for the page. This is inclusive of sessions that started on different pages, as well as “bounce” sessions that start and end on the same page. For all pageviews to the page, Exit Rate is the percentage that were the last in the session. Year: Calendar year over which the data was collected. Data reflects the counts for each metric from January 1st through December 31st.

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

Abstract
Data Set Characteristics:	Multivariate	Number of Instances:	65532	Area:	Computer
Attribute Characteristics:	N/A	Number of Attributes:	12	Date Donated	2019-02-04
Associated Tasks:	Classification	Missing Values?	N/A	Number of Web Hits:	701

Source:

Fatih Ertam, fatih.ertam '@' firat.edu.tr, Firat University, Turkey.

Data Set Information:

There are 12 features in total. Action feature is used as a class. There are 4 classes in total. These are allow, action, drop and reset-both classes.

Attribute Information:

Source Port,Destination Port,NAT Source Port,NAT Destination Port,Action,Bytes,Bytes Sent,Bytes Received,Packets,Elapsed Time (sec),pkts_sent,pkts_received

Relevant Papers:

F. Ertam and M. Kaya, “Classification of firewall log files with multiclass support vector machine,†in 6th International Symposium on Digital Forensic and Security, ISDFS 2018 - Proceeding, 2018.

Dataset

This dataset was created by Sandeep K

Contents

It contains the following files:

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

This data shows the number of hits each page gets on the City of Bloomington website. The data is pulled from Google analytics.

Instructions for exporting data from Google Analytics are available here

This is a dataset consisting of features for tracks fetched using Spotify's Web API. The tracks are labeled '1' or '0' ('Hit' or 'Flop') depending on some criterias of the author. This dataset can be used to make a classification model that predicts whethere a track would be a 'Hit' or not. (Note: The author does not objectively considers a track inferior, bad or a failure if its labeled 'Flop'. 'Flop' here merely implies that it is a track that probably could not be considered popular in the mainstream.) Here's an implementation of this idea in the form of a website that I made. {http://www.hitpredictor.in/}

In March 2024, search platform Google.com generated approximately 85.5 billion visits, down from 87 billion platform visits in October 2023. Google is a global search platform and one of the biggest online companies worldwide.

In 2023, most of the global website traffic was still generated by humans but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 32 percent of global web traffic in the most recently measured period, representing an increase of 1.8 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past two years, indicating a surge in the sophistication of cyber threats. Simultaneously, simple bad bots saw a 6 percent increase compared to the previous year, suggesting a shift in the landscape of automated threats. Meanwhile, areas like entertainment, and law & government face the highest amount of advanced bad bots, with more than 78 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 57.2 percent of the gaming segment's web traffic. Meanwhile, almost half of the online traffic for telecom and ISPs was moved by malicious applications. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and financial services experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

Report Overview

The AI and RAN Traffic Optimization Market is projected to soar from USD 2.2 billion in 2024 to an impressive USD 27.2 billion by 2034, reflecting a robust compound annual growth rate (CAGR) of 28.60% during the forecast period from 2025 to 2034. In 2024, North America holds a commanding lead in the market, accounting for over 44.8% of the global share, with revenues reaching USD 0.9 billion, solidifying its dominant position in this rapidly expanding sector.

Artificial intelligence (AI) in Radio Access Network (RAN) traffic optimization represents a significant evolution in telecommunications, enhancing network management and service quality. This integration of AI with RAN optimizes data traffic flow across networks, ensuring efficient use of network resources, minimizing congestion, and improving user experience. In addition to optimizing data traffic flow, AI in Radio Access Network (RAN) traffic optimization also enables predictive maintenance and real-time network analytics.

The AI and RAN Traffic Optimization market is experiencing rapid growth, driven by several key factors. As data traffic surges due to the rise of internet-connected devices, video streaming, and real-time data needs, the demand for more sophisticated network management solutions has never been greater. AI’s ability to predict network loads and optimize data routing not only reduces latency but also significantly enhances the reliability of mobile networks. At the same time, the escalating threat of cyberattacks is spurring the adoption of AI, which plays a crucial role in strengthening cybersecurity.

https://techmarketreports.com/wp-content/uploads/2025/01/AI-and-RAN-Traffic-Optimization-market-size-1024x593.jpg" alt="AI and RAN Traffic Optimization market size" class="wp-image-139036">

Emerging trends in AI and RAN include the deployment of machine learning algorithms for predictive maintenance, which anticipates and rectifies network faults before they affect service. The growth of 5G technology accelerates the adoption of AI in RAN, with AI-enabled applications such as dynamic spectrum management and energy efficiency improvements becoming increasingly prevalent. Moreover, AI facilitates the shift towards virtualized RAN (vRAN) architectures that offer enhanced scalability and flexibility​.

Implementing AI in RAN optimization brings a wide array of business advantages. By automating key processes, telecom operators can significantly cut operational costs while boosting efficiency. The result is not only enhanced service reliability and connectivity, which directly improves customer satisfaction, but also a greater ability to scale operations swiftly. This flexibility allows operators to innovate their service offerings at a faster pace, giving them a competitive edge in the rapidly evolving telecommunications market.

Facebook is a web traffic powerhouse: in March 2024 approximately 16.6 billion visits were measured to the Facebook.com, making it one of the most-visited websites online. In the third quarter of 2023, Facebook had nearly three billion monthly active users.

In April 2025, the online traffic to groupon.com hit **** million visits. This was a slight decrease of monthly visits since March 2025, when the flash shopping platform Groupon had **** million visits.

Compensation Case Information Item (Foundation Special Compensation Fund)