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.

Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

This statistic displays the daily average number of unique visitors to the MNM website in Belgium from 2010 to 2018. Following a significant increase in the daily number of people who visited the MNM website in 2017, the MNM websites visitor numbers decreased in 2018. The website had an average of roughly ****** daily visitors in 2017, whereas in 2018 this figure decreased to roughly ****** visitors.

Comprehensive dataset analyzing Walmart.com's daily website traffic, including 16.7 million daily visits, device distribution, geographic patterns, and competitive benchmarking data.

In March 2024, X's web page Twitter.com had *** billion website visits worldwide, up from *** billion site visits the previous month. Formerly known as Twitter, X is a microblogging and social networking service that allows most of its users to write short posts with a maximum of 280 characters.

Top 25 Daily Page Views for the main website of Los Angeles

Comprehensive dataset analyzing Amazon's daily website visits, traffic patterns, seasonal trends, and comparative analysis with other ecommerce platforms based on May 2025 data.

Unlock insights with Echo's Activity data, offering views of locations based on visitor behavior. Enhance site selection, urban planning, and real estate with metrics like unique visitors and visits. Our high-quality, global data reveals movement patterns, updated daily and normalized monthly.

Visitors Statistics Open Data MFSR - Website traffic statistics by country (daily)

Global Same-Day Visitors by Country, 2023 Discover more data with ReportLinker!

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.

Comprehensive dataset analyzing eBay's daily visitor traffic patterns, geographic distribution, device usage, and competitive positioning based on third-party analytics from Similarweb and Semrush.

These data represent the number of overnight tourists and visitors per month

Global Number of Excursionists (Same-Day Visitors) by Country, 2023 Discover more data with ReportLinker!

Forecast: Inbound Same-Day Visitors in the US 2024 - 2028 Discover more data with ReportLinker!

A visitor is classified as a tourist (or an overnight visitor) if his trip includes one night's stay, and the same-day visitor does not include an overnight stay.Source: Inbound Tourism Survey

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Traffic monitoring data is a strategic resource for SHA and Maryland's Department of Transportation. The data is essential in the planning - design and operation of the statewide road system and the development and implementation of state highway improvement and safety programs. TMS is a product of the ISTEA Act of 1991 - which required a traffic data program to effectively and efficiently meet SHA's long-term traffic data monitoring and reporting requirements. The quality control feature of the system allow data edit checks and validation for data from the 84 permanent - continuous automatic traffic recorders (ATRs) and short-term traffic counts.The Maryland Traffic Volume Maps depict the Annual Average Daily Traffic (AADT) at various locations on Maryland's roadways by county. Traffic Volume data is collected from over 8700 program count stations and 84 ATRs - located throughout Maryland. To date - four (4) ATRs have been removed from the ATR Program. Program count data is collected (both directions) at regular locations on either a three (3) year or six (6) year cycle depending on type of roadway. Growth Factors are applied to counts which were not taken during the current year and the counts are factored based on the past yearly growth of an associated ATR. Counters are placed for 48 hours on a Monday or Tuesday and are picked up that Thursday or Friday - respectively. The ATR and toll count data is collected on a continuous basis. Toll station data is provided by the Maryland Transportation Authority. A special numeric code was added to the AADT numbers - starting in 2006 - to identify the years when the count was actually taken. The last digit represents the number of years prior to the actual count. Where '0' represents the current year when data was collected (in 2014) - '1' represents the count taken in 2013 - '2' represents the count taken in 2012 - '3' represents the count taken in 2011 and so forth. Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Transportation/MD_AnnualAverageDailyTraffic/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.