Context

The Monthly State Retail Sales (MSRS) is the Census Bureau's new experimental data product featuring modeled state-level retail sales. This is a blended data product using Monthly Retail Trade Survey data, administrative data, and third-party data. Year-over-year percentage changes are available for Total Retail Sales excluding Non-store Retailers as well as 11 retail North American Industry Classification System (NAICS) retail subsectors. These data are provided by state and NAICS codes beginning with January 2019.

Content

Geography: US

Time period: 2019 - 2022

Unit of analysis: US Census Bureau's Monthly State Retail Sales Data

Variables

Acknowledgements

Datasource: United States Census Bureau's Monthly State Retail Sales

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This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Use this data dictionary to identify what field names mean in the PCWEBF921 Parcel Sales Information Table from the Tax System.

The tables presents indices (2005=100) and changes on twelve months previously (%) of production, turnover and orders in industry (excl. construction), by sector of industry.

Data available : January 2000 till December 2012

Table has been discontinued as from 22 March 2013 due to change of the base year from 2005 to 2010. Statistics Netherlands has started a new table, Industry; production, sales and orders, changes and index (2010 = 100). For more information see sections 3 and 4.

Status of the figures: Production: three most recent months: provisional. The figures within a reporting year are revised provisional figures until publication in December of the year concerned. Turnover: three most recent months: provisional. Orders: three most recent months: provisional.

Changes as of 8 July 2011. Due to new regulations (European System for National Accounts, 2010, Balance of Payments Manual 6) for National Accounts and Balance of Payment, the turnover definition has been adapted. This results in adjustments in production index and other short term statistics. The adaptation of the turnover definition is related to a change in registration of enterprises that (partially) contract out their production abroad. The adjustment means that goods dealt with by foreign subsidiaries of Dutch parent companies do count for Dutch production. Goods dealt with in the Netherlands by Dutch subsidiaries of foreign parent companies that remain property of these parent companies do no longer count as Dutch production. However, they count as export of services for the sum that has been added to value in the Netherlands. Until December 2009, index figures for manufacturing turnover are based on the previous turnover definition. From January 2010 onwards, the turnover figures are based on the new turnover definition. Therefore, turnover changes 2010 on 2009 are not accurate.

Vietnam Retail Sales: BP: Hanoi: Goods: Means of Transport data was reported at 12,030.000 VND bn in 2023. This records an increase from the previous number of 11,366.000 VND bn for 2022. Vietnam Retail Sales: BP: Hanoi: Goods: Means of Transport data is updated yearly, averaging 11,649.000 VND bn from Dec 2018 (Median) to 2023, with 6 observations. The data reached an all-time high of 12,089.000 VND bn in 2020 and a record low of 9,946.000 VND bn in 2021. Vietnam Retail Sales: BP: Hanoi: Goods: Means of Transport data remains active status in CEIC and is reported by Hanoi Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.H010: Retail Sales: Hanoi: Annual.

Mexico Sales: CM: Magnetic & Optic Means data was reported at 332.413 MXN mn in Feb 2025. This records a decrease from the previous number of 341.186 MXN mn for Jan 2025. Mexico Sales: CM: Magnetic & Optic Means data is updated monthly, averaging 307.974 MXN mn from Jan 2018 (Median) to Feb 2025, with 86 observations. The data reached an all-time high of 497.465 MXN mn in Oct 2022 and a record low of 189.226 MXN mn in Feb 2020. Mexico Sales: CM: Magnetic & Optic Means data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.C001: Manufacturing Sales.

This table has been discontinued due to a shift in the base year.

This table presents information about developments in production and turnover in industry (excl. construction), SIC 2008 sections B - E. The data can be divided by a number of branches according to Statistics Netherlands' Standard Industrial Classification of all Economic Activities 2008 (SIC 2008). Developments are presented as percentage changes compared to a previous period and by means of indices. In this table, the base year is updated to 2015, in previous publications the base year was 2010.

Developments in turnover and volume are published in two formats. Firstly, in the form of year-on-year changes relative to the same period in the preceding year. These figures are shown both unadjusted and adjusted for calendar effects. The second format pertains to period-on-period changes, for example quarter-on-quarter. Period-on-period changes are calculated by applying seasonal adjustment.

Data available from January 2005 up and until December 2023.

Status of the figures: The figures of a calendar year will become definite no later than five months after the end of that calendar year. Until then, the figures in this table will be “provisional” and can still be adjusted as a result of delayed response. Currently, the monthly turnover figures of 2022 are definitive. Once definitive figures have been published, Statistics Netherlands will only revise the results if significant adjustments and/or corrections are necessary. Since this table has been discontinued, the data will not be finalized.

Changes as of 14 February 2024: The figures of December 2023 have been added to the table and those of September up to and including November 2023 have been adjusted and this table has been discontinued.

Changes as of 9 June 2023: The figures of April 2023 have been added to the table and those of January 2022 up to and including March 2023 have been adjusted. This month the annual update of the seasonal-adjustment models has taken place. All figures of 2022 have been revised for the final time and set to ''definitive'' status.

Changes as of 10 June 2021: The figures of April 2021 have been added to the table. The figures of January 2020 up to and including March 2021 have been adjusted. This month the annual update of the seasonal-adjustment models has taken place. Because of additional changes that have been made due to Covid-19 the adjustments are a bit larger than in other years. All figures of 2020 have been revised for the final time and set to ''definitive'' status.

The underlying coding of the following classifications used in this table has been adjusted: - Manufacture of capital goods - Manufacture of consumer goods - Manufacture of durable consumer goods - Manufacture of intermediate goods - Manufacture of non-durable consumergoods

It is now in line with the standard encoding defined by CBS. The structure and data of the table have not been adjusted.

When will new figures be published? No longer applicable.

This table is succeeded by "Industry; production and sales, changes and index, 2021=100". See Section 3.

United States GDP: PI: sa: Implicit Price Def for Final Sales to DomPurchasers data was reported at 125.924 2017=100 in Mar 2025. This records an increase from the previous number of 124.884 2017=100 for Dec 2024. United States GDP: PI: sa: Implicit Price Def for Final Sales to DomPurchasers data is updated quarterly, averaging 51.772 2017=100 from Mar 1947 (Median) to Mar 2025, with 313 observations. The data reached an all-time high of 125.924 2017=100 in Mar 2025 and a record low of 10.879 2017=100 in Mar 1947. United States GDP: PI: sa: Implicit Price Def for Final Sales to DomPurchasers data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A039: NIPA 2023: GDP by Expenditure: Price Index: 2017=100: Seasonally Adjusted.

In 2024, Market Research Intellect valued the Diesel Exhaust Fluid Def Heater Market Report at USD 2.5 billion, with expectations to reach USD 4.2 billion by 2033 at a CAGR of 7.5%.Understand drivers of market demand, strategic innovations, and the role of top competitors.

Global sales of 4k ultra-high-definition televisions are forecast to amount to over 100 million units for the first time in 2019. Ever since the technology began to enter mainstream use, unit sales have increased rapidly with each passing year. Total unit sales have grown tenfold from around ** million units in 2014 to an estimated *** million that are expected in 2019.

Ultra HD Televisions

4K televisions fall under an image resolution standard called ultra-high-definition (UHD), which gets its name from the fact that UHD resolution screens have nearly ***** horizontal pixels. Once a format used almost exclusively in film production, 4K resolution is now becoming a somewhat standard feature in high-end TVs, boasting a U.S. household penetration rate of nearly one third in 2018. As with all innovations in television resolution and picture quality, the benefit of the hardware relies on entertainment industry productions that make use of these higher resolutions. As an increasing number of shows and streaming services are supporting UHD resolution, the advantages of UHD over older resolution standards becomes more apparent to consumers and hence drives demand for the product. Today, over half of the new TVs which are shipped around the world falls under the UHD category, with that share being even higher in places like the U.S., China, and Western Europe.

Key Table Information.Table Title.Wholesale Trade: Sales and Commissions of Electronic Markets, Agents, Brokers, and Commission Merchants for the U.S.: 2022.Table ID.ECNCOMM2022.EC2242COMM.Survey/Program.Economic Census.Year.2022.Dataset.ECN Sector Statistics Sector 42: Wholesale Trade.Source.U.S. Census Bureau, 2022 Economic Census, Sector Statistics.Release Date.2025-07-10.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of establishmentsSales, value of shipments, or revenue ($1,000)Sales on own account ($1,000)Sales made on the account of others ($1,000)Sales made on the account of others as percent of total sales, value of shipments, or revenue (%)Commissions received for sales made on the account of others ($1,000)Commissions received for sales made on the account of others as percent of sales on the account of others (%)Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 3- through 8-digit 2022 NAICS code levels within subsector 425. For information about NAICS, see Economic Census Code Lists..Business Characteristics.For Wholesale Trade (42), data are presented by Type of Operation (All establishments) only..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For some data on this table, estimates come only from the establishments selected into the sample. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.For some data on this table, estimates come only from the establishments selected into the sample. For these estimates, selected establishments have sampling weights equal to the inverse of their selection probability, generally between 1 and 40. There is further weighting to account for nonresponse and to ensure that detailed estimates sum to basic statistics where applicable. For more information on weighting, see 2022 Economic Census Methodology..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector42/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing da...

Discover the booming electronic dictionary market! Our comprehensive analysis reveals a 12.7% CAGR through 2033, driven by language learning needs and technological advancements. Explore market size, segmentation, key players (Casio, Seiko, Sharp), and regional trends in this in-depth report.

These statistics relate only to sales by local authorities under the Right to Buy scheme and exclude sales by Private Registered Providers (PRPs) under preserved Right to Buy. Sales by PRPs are recorded in Social Housing Sales

The sales figures exclude Right to Buy sales of dwellings which are not accounted for in a local authority’s Housing Revenue Account, either because the authority, having disposed of nearly all its dwellings to a registered provider, has closed down its Housing Revenue Account or because the dwelling was originally tied to a particular occupation (e.g. a school caretaker’s cottage or a park keeper’s cottage).

The figures also exclude any Right to Buy sales of dwellings which, although accounted for in the Housing Revenue Account, are the subject of an agreement made either under section 80B of the Local Government and Housing Act 1989 (as inserted by section 313 of the Housing and Regeneration Act 2008 and now repealed) or under section 11(6) of the Local Government Act 2003 (as inserted by section 174 of the Localism Act 2011).

The figures include sales at less than market value of dwellings accounted for in the Housing Revenue Account to secure tenants of a local authority, even when those sales are not under Right to Buy.

Some figures will include proportions of dwellings. This is because the figures also include sales of a shared ownership lease of a dwelling accounted for in the Housing Revenue Account where either the premium (i.e. a portion of the market value of the dwelling) paid by the purchaser exceeded 50% of the market value of the dwelling or the sum of the premium paid by the purchaser and all other premiums paid up to two years before the payment of the current premium. Where a shared ownership disposal has been included, the figure corresponds to the portion of the market value paid; for example the purchase of a 50% equity share will be represented by 0.5.

For detailed definitions, see definitions in Right to Buy Statistics.

Data are collected from a quarterly local authority return to the DCLG called LOGASNet. Local authorities with dwelling stock which receive poolable housing receipts supply these data to DCLG on a quarterly basis.

These data are taken directly from the Social Housing Sales data set, Live Table 691

Please note that figures published in the live tables are organised into financial quarters, i.e. 2013/2014 financial Q1 corresponds to April-June 2013, whereas figures are published here in calendar quarter intervals, where 2013 calendar Q1 corresponds to the interval Jan – March 2013.

If data is not provided for a local authority this is either due to the authority not owning dwelling stock, or the reporting boundaries changing and thereby causing groups of authorities in the affected areas to be reclassified and consequently not reporting data for specific time periods.

Context

Used in Businesses for segmentation by using various clustering techniques

Inspiration

Learn about K-means , PCA and autoencoders

Context

Supplement Sales Prediction

• Your Client WOMart is a leading nutrition and supplement retail chain that offers a comprehensive range of products for all your wellness and fitness needs.

• WOMart follows a multi-channel distribution strategy with 350+ retail stores spread across 100+ cities.

• Effective forecasting for store sales gives essential insight into upcoming cash flow, meaning WOMart can more accurately plan the cashflow at the store level.

• Sales data for 18 months from 365 stores of WOMart is available along with information on Store Type, Location Type for each store, Region Code for every store, Discount provided by the store on every day, Number of Orders everyday etc.

• Your task is to predict the store sales for each store in the test set for the next two months.

Content

Train Data |Variable |Definition | |-------------------------------|-------------------------------| |ID |Unique Identifier for a row | |Store_id |Unique id for each Store| |Store_Type |Type of the Store| |Location_Type |Type of the location where Store is located| |Region_Code |Code of the Region where Store is located| |Date |Information about the Date| |Holiday |If there is holiday on the given Date, 1 : Yes, 0 : No| |Discount |If discount is offered by store on the given Date, Yes/ No| |#Orders |Number of Orders received by the Store on the given Day| |Sales |Total Sale for the Store on the given Day|

Test Data |Variable |Definition | |-----------------------------|-------------------------| |ID |Unique Identifier for a row | |Store_id |Unique id for each Store | |Store_Type |Type of the Store | |Location_Type |Type of the location where Store is located | |Region_Code |Code of the Region where Store is located | |Date |Information about the Date | |Holiday |If there is holiday on the given Date, 1 : Yes, 0 : No | |Discount |If discount is offered by store on the given Date, Yes/ No |

Sample_Submission |Variable |Definition | |------------------------|----------------| |ID |Unique Identifier for a row | |Sales |Total Sale for the Store on the given Day |

Evaluation

• The evaluation metric for this competition is MSLE * 1000 across all entries in the test set.

Public and Private Split

• Test data is further divided into Public (First 20 Days) and Private (Last 41 Days). You will make the prediction for two months (61 days).
• Your initial responses will be checked and scored on the Public data.
• The final rankings would be based on your private score which will be published once the competition is over.

The sales column that we submit would be compared to the actual answer similar to the following. Instead of 8 items it is 22266 items(the function is avable in sklearn).

Sample Input :

actual = [27.5, 55.9, 25.8, 17.7, 27.6, 55.9, 25.7, 17.8] predicted = 24.0, 49.1, 21.0, 16.2, 23.3, 47.0, 12.1, 15.2*1000

Sample Output:

82.9949678377161

Public and Private Split

• Test data is further divided into Public (First 20 Days) and Private (Last 41 Days). You will make the prediction for two months (61 days).

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Retail Sales Control Group in the United States decreased to -0.10 percent in September from 0.60 percent in August of 2025. This dataset includes a chart with historical data for the United States Retail Sales Control Group MoM.

Residential market value estimates and most recent sales values for owned properties at a parcel level within Fairfax County as of the VALID_TO date in the attribute table.

For methodology and a data dictionary please view the IPLS data dictionary

📖 Overview

(If its helpful kindly support by upvoting the dataset)

This dataset contains a detailed record of sales and movement data by item and department from Montgomery County, Maryland. It is updated monthly and includes information on warehouse and retail liquor sales.

📑 Data Dictionary

The dataset can be used for:

📊 Time-series or trend analysis of product sales 🧾 Retail forecasting and demand estimation 🗺️ Regional economic and consumption studies

🧩 Data Summary

Source: Montgomery County Open Data Portal Publisher: Montgomery County of Maryland — data.montgomerycountymd.gov Maintainer: svc dmesb (no-reply@data.montgomerycountymd.gov) Category: Community / Recreation Update Frequency: Monthly First Published: July 6, 2017 Last Updated: September 5, 2025

⚖️ License & Usage

This dataset is publicly accessible under the Montgomery County, Maryland Open Data Terms of Use. It is a non-federal dataset and may have different terms of use than Data.gov datasets. No explicit license information is provided by the source. Use responsibly and always cite the original source below when reusing the data.

🙌 Credits

Dataset originally published by: Montgomery County of Maryland 📍 https://data.montgomerycountymd.gov

📄 Source Page: Warehouse and Retail Sales