United States Off Within 2 Weeks: sa: All Residential: Newark, NJ data was reported at 40.896 % in Jul 2020. This records an increase from the previous number of 39.285 % for Jun 2020. United States Off Within 2 Weeks: sa: All Residential: Newark, NJ data is updated monthly, averaging 23.657 % from May 2015 (Median) to Jul 2020, with 63 observations. The data reached an all-time high of 40.896 % in Jul 2020 and a record low of 16.214 % in Apr 2020. United States Off Within 2 Weeks: sa: All Residential: Newark, NJ data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

United States Off Within 2 Weeks: All Residential: New York, NY data was reported at 24.912 % in Jul 2020. This records a decrease from the previous number of 30.188 % for Jun 2020. United States Off Within 2 Weeks: All Residential: New York, NY data is updated monthly, averaging 17.076 % from May 2015 (Median) to Jul 2020, with 63 observations. The data reached an all-time high of 30.834 % in Jun 2017 and a record low of 8.213 % in Apr 2020. United States Off Within 2 Weeks: All Residential: New York, NY data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB010: Off Market Within 2 Weeks: by Metropolitan Areas.

India's main stock market index, the SENSEX, fell to 85138 points on December 2, 2025, losing 0.59% from the previous session. Over the past month, the index has climbed 1.38% and is up 5.31% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The Economic Indicator Service (EIS) aims to deliver economic content to financial institutions on both buy and sell-side and service providers. This new service currently covers 34,351 recurring macro-economic indicators from 135 countries ( as of December 16, 2019 ) such as GDP data, unemployment releases, PMI numbers etc.

Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.

Depending on availability, information regarding economic indicators, including the details of the issuing agency as well as historical data series can be made accessible for the client. Key information about EIS: • Cloud-based service for Live Calendar – delivered via HTML/JavaScript application formats, which can then be embedded onto any website using iFrames • Alternatives methods available – such as API and JSON feed for the economic calendar that can be integrated into the company’s system • Live data – updated 24/5, immediately after the data has been released • Historical data – includes a feed of all previous economic indicators available We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. The calendar includes the following. • Recurring & Non-recurring indicators covering 136 countries across 21 regions. • Indicators showing high, medium, and low impact data. • Indicators showing actual, previous, and forecast data. • Indicators can be filtered across 16 subtypes. • News generation for selected high-impact data. • Indicator description and historical data up to the latest eight historical points with a chart.

United States Off Within 2 Weeks: Townhouse: Brainerd, MN data was reported at 71.429 % in Jul 2020. This records an increase from the previous number of 40.000 % for Jun 2020. United States Off Within 2 Weeks: Townhouse: Brainerd, MN data is updated monthly, averaging 33.333 % from Mar 2012 (Median) to Jul 2020, with 90 observations. The data reached an all-time high of 100.000 % in Feb 2020 and a record low of 0.000 % in Dec 2019. United States Off Within 2 Weeks: Townhouse: Brainerd, MN data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB010: Off Market Within 2 Weeks: by Metropolitan Areas.

Euro Area's main stock market index, the EU50, rose to 5684 points on December 2, 2025, gaining 0.27% from the previous session. Over the past month, the index has climbed 0.09% and is up 16.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on December of 2025.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Thomas Le on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

Note: This resource was originally published on opengov.nsw.gov.au. The OpenGov website has been retired. If you have any questions, please contact the Agency Services team at transfer@mhnsw.au

Agency

• Treasury

The primary function of the Livestock and Grain Market News Division of the Livestock and Seed Program (LSP) is to compile and disseminate information that will aid producers, consumers, and distributors in the sale and purchase of livestock, meat, grain, and their related products nationally and internationally.

Off Within 2 Weeks: sa: Single Family: Lebanon, NH data was reported at 17.000 % in Apr 2020. This records a decrease from the previous number of 17.100 % for Mar 2020. Off Within 2 Weeks: sa: Single Family: Lebanon, NH data is updated monthly, averaging 9.800 % from Feb 2012 (Median) to Apr 2020, with 99 observations. The data reached an all-time high of 23.300 % in Feb 2020 and a record low of -2.200 % in Apr 2014. Off Within 2 Weeks: sa: Single Family: Lebanon, NH data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

Note: This resource was originally published on opengov.nsw.gov.au. The OpenGov website has been retired. If you have any questions, please contact the Agency Services team at transfer@mhnsw.au

Agency

• Treasury

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

Note: This resource was originally published on opengov.nsw.gov.au. The OpenGov website has been retired. If you have any questions, please contact the Agency Services team at transfer@mhnsw.au

Agency

• Treasury

Private businesses in the United States hired 42 thousand workers in October of 2025 compared to -29 thousand in September of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Socioeconomic dataset for analysing demand prediction of weekend markets in the city of Hamburg, Germany

In this DDLitlab funded Data Literacy student project, our goal was to predict weekend markets in the city of Hamburg and using open-source data and OpenStreetMaps in conjunction with Machine Learning Algorithms. You can find a brief article about the initial grant and our approach here : https://www.cliccs.uni-hamburg.de/about-cliccs/news/2023-news/2023-08-24-ddlitlab-event.html

Github repository: https://gitlab.rrz.uni-hamburg.de/exploring-avenues-for-the-deployment-of-machine-learning-algorithms-for-sustainable-small-agricultural-business-information-using-openstreetmap/main-project-v-3

This repository is intended to make our codes and visualisations openly available to the University of Hamburg students for further research. This is not to be used without citation under any circumstances and the University/authors deserve the right to withdraw consent at any time.

Please do not forget to cite our work in the event of fair use.

Organisation of our Github repository

Codes: contains the codes for the different methods deployed for data preparation,variable selection,visualisations showing the spatial characteristics of our variables, calculating indices such as correlation coefficients and machine learning methods in increasing order of complexity. City-district (Stadtteil) as the unit of analysis.

Data (uploaded datasets) : The open source data obtained for the project has been obtained from OpenStreetMaps (https://wiki.openstreetmap.org/wiki/Use_OpenStreetMap ) and Statistik Nord (https://www.statistik-nord.de/ ) . Each variable contains values for all stadtteils (city-districts) of Hamburg. The filenames are self explanatory.

The Hamburg shapefile has been obtained from Geofabrik https://www.geofabrik.de/de/data/shapefiles.html In addition to the original data uploaded in the section, we have also laid down the final data we have deployed with the algorithms, in the final final_data.csv

Our repository contains the following additional sections:

Results: This section contains results from the codes processed in the first section. It includes the final 10 variables selected for the study, the results from the VIF analysis, correlation matrix, and some model output statistics.

Visualisations: This section is dedicated to visualisations of the variables used for the study and the results from deployment of various methods. In case of any questions, please do not hesitate to contact us at our official student IDs : first.lastname@studium.uni-hamburg.de. We are also available on LinkedIn for professional networking in case of other queries.

Data curators /DDLitLab data literacy project team

Ferdinand Hölzl

Leidy Gicela Vergara Lopez

Shivanshi Asthana

Shuyue Qu

Sojung Oh

Juan Miguel Rodriguez Lopez

Techsalerator's News Events Data for Argentina: A Comprehensive Overview

Techsalerator's News Events Data for Argentina offers a powerful resource for businesses, researchers, and media organizations. This dataset compiles information on significant news events across Argentina, pulling from a wide range of media sources, including news outlets, online publications, and social platforms. It provides valuable insights for those looking to track trends, analyze public sentiment, or monitor industry-specific developments.

Key Data Fields

1. Event Date: Captures the exact date of the news event. This is crucial for analysts who need to monitor trends over time or for businesses responding to market shifts.

2. Event Title: A brief headline describing the event. This allows users to quickly categorize and assess news content based on relevance to their interests.

3. Source: Identifies the news outlet or platform where the event was reported. This helps users track credible sources and assess the reach and influence of the event.

4. Location: Provides geographic information, indicating where the event took place within Argentina. This is especially valuable for regional analysis or localized marketing efforts.

5. Event Description: A detailed summary of the event, outlining key developments, participants, and potential impact. Researchers and businesses use this to understand the context and implications of the event.

Top 5 News Categories in Argentina

1. Politics: Major news coverage on government decisions, political movements, elections, and policy changes that affect the national landscape.

2. Economy: Focuses on Argentina’s economic indicators, inflation rates, international trade, and corporate activities influencing business and finance sectors.

3. Social Issues: News events covering protests, public health, education, and other societal concerns that drive public discourse.

4. Sports: Highlights events in football, rugby, and other popular sports, often drawing widespread attention and engagement across the country.

5. Technology and Innovation: Reports on tech developments, startups, and innovations in Argentina’s growing tech ecosystem, featuring companies like Mercado Libre and Globant.

Top 5 News Sources in Argentina

1. Clarín: One of Argentina's leading newspapers, offering in-depth coverage of politics, economy, and social issues.

2. La Nación: A well-respected source for news related to politics, business, and cultural events across the country.

3. Infobae: A prominent online news platform providing real-time updates on breaking news, sports, and entertainment.

4. Página/12: A left-leaning publication that covers national politics, social issues, and investigative journalism.

5. TN (Todo Noticias): A major news network that broadcasts updates on current affairs, sports, and live events throughout Argentina.

Accessing Techsalerator’s News Events Data for Argentina

To access Techsalerator’s News Events Data for Argentina, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Event Date
• Event Title
• Source
• Location
• Event Description
• Event Category (Politics, Economy, Sports, etc.)
• Participants (if applicable)
• Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is an invaluable tool for keeping track of significant events in Argentina. It aids in making informed decisions, whether for business strategy, market analysis, or academic research, providing a clear picture of the country’s news landscape.

Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.

The dataset holds 11,932 documents annotated with 3 labels:

sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" }

The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

United States Off Within 2 Weeks: All Residential: Ogden, UT data was reported at 32.861 % in Jul 2020. This records a decrease from the previous number of 54.406 % for Jun 2020. United States Off Within 2 Weeks: All Residential: Ogden, UT data is updated monthly, averaging 37.533 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 62.220 % in Apr 2018 and a record low of 2.688 % in Jun 2014. United States Off Within 2 Weeks: All Residential: Ogden, UT data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB010: Off Market Within 2 Weeks: by Metropolitan Areas.

Weekly newsletter containing economic commentary, analysis and statistics examining Alberta’s economy, labour market, price indices, household sector and business sector.

Stock market forecasting remains a complex and challenging task to forecast, traditional technical analysis methods like RSI, EMA, and Candlestick Patterns often fail to analyze the stock market time series pattern with many recent studies have now explored forecasting using machine learning or neural networks, other studies have improved the increase in accuracy or decrease in regression loss by applying technical indicator and sentiment analysis. This paper aims to analyze the performance of the combined reinforcement learning and machine learning models in predicting the stock market’s next day trend by incorporating both technical and sentiment-based features. Technical indicators were derived from historical price data focused on multi-timeframe trend and swing trend in the market, then sentiment features were extracted using FinBERT from Benzinga Pro as a reliable financial news source. The reinforcement learning model used is the Proximal Policy Optimization model, while a variety of machine learning models, such as XGBoost, Gradient Boosting, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression were trained to assess its predictive performance. Results indicate that the ensemble model outperformed the other tested machine learning models with an accuracy score of 69.97%. These reports highlight the effectiveness of the ensemble model combining sentiment and technical features to enhance stock market predictions accuracy. However, limitations such as news data availability and the small training time, remain a key challenge that could potentially increase the performance. Future research could experiment with alternative models, more training time, advance technical patterns, and more news datasets.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.