The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States CSI: Expected Interest Rates: Next Yr: Go Down data was reported at 4.000 % in May 2018. This records a decrease from the previous number of 6.000 % for Apr 2018. United States CSI: Expected Interest Rates: Next Yr: Go Down data is updated monthly, averaging 11.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 54.000 % in Jun 1980 and a record low of 3.000 % in May 2014. United States CSI: Expected Interest Rates: Next Yr: Go Down data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

These rates are the daily secondary market quotation on the most recently auctioned Treasury Bills for each maturity tranche (4-week, 13-week, 26-week, and 52-week) that Treasury currently issues new Bills. Market quotations are obtained at approximately 3:30 PM each business day by the Federal Reserve Bank of New York. The Bank Discount rate is the rate at which a Bill is quoted in the secondary market and is based on the par value, amount of the discount and a 360-day year. The Coupon Equivalent, also called the Bond Equivalent, or the Investment Yield, is the bill's yield based on the purchase price, discount, and a 365- or 366-day year. The Coupon Equivalent can be used to compare the yield on a discount bill to the yield on a nominal coupon bond that pays semiannual interest.

Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data was reported at 6.566 % in Mar 2025. This records an increase from the previous number of 6.446 % for Dec 2024. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data is updated quarterly, averaging 6.330 % from Mar 2012 (Median) to Mar 2025, with 53 observations. The data reached an all-time high of 6.961 % in Sep 2023 and a record low of 4.454 % in Mar 2022. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SE003: Banking Survey: Interest Rate. [COVID-19-IMPACT]

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

FocusEconomics' economic data is provided by official state statistical reporting agencies as well as our global network of leading banks, think tanks and consultancies. Our datasets provide not only historical data, but also Consensus Forecasts and individual forecasts from the aformentioned global network of economic analysts. This includes the latest forecasts as well as historical forecasts going back to 2010. Our global network consists of over 1000 world-renowned economic analysts from which we calculate our Consensus Forecasts. In this specific dataset you will find economic data for Japan Interest Rate.

Weekly updated dataset of Nationwide Building Society mortgage products, including interest rates, LTVs, APRC and product fees.

FocusEconomics' economic data is provided by official state statistical reporting agencies as well as our global network of leading banks, think tanks and consultancies. Our datasets provide not only historical data, but also Consensus Forecasts and individual forecasts from the aformentioned global network of economic analysts. This includes the latest forecasts as well as historical forecasts going back to 2010. Our global network consists of over 1000 world-renowned economic analysts from which we calculate our Consensus Forecasts. In this specific dataset you will find economic data for South Africa Interest Rate.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Machine Learning Predicts QQQ to Increase in Value by 5% in the Next 3 Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In-Memory Data Grids Market Outlook

The global In-Memory Data Grids market size is projected to grow from $2.5 billion in 2023 to an estimated $4.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 7.5%. This impressive growth trajectory is driven by the increasing demand for real-time data processing capabilities across various industries, necessitating faster data storage and retrieval solutions. The enhanced speed and performance of in-memory data grids are crucial as businesses strive for efficiency in data management, contributing to a robust market expansion over the forecast period.

One of the primary growth factors for the In-Memory Data Grids market is the escalating volume of data generated globally, which necessitates more efficient data management solutions. Organizations across sectors such as retail, finance, and healthcare are increasingly focused on harnessing data for strategic insights, which in turn fuels demand for advanced data processing tools. In-memory data grids provide a high-performance solution for handling large datasets, allowing for faster data access and manipulation, and are therefore becoming integral to modern data strategies. Moreover, as businesses continue to explore big data analytics, the need for systems that can support real-time analytics is propelling the market further.

The rise of digital transformation initiatives across various industries is another significant factor driving the in-memory data grids market. Companies are increasingly adopting digital technologies to enhance operational efficiencies, improve customer experiences, and maintain competitive advantage. In-memory data grids serve as a critical infrastructure component in these digital transformation efforts by enabling rapid data processing and supporting real-time decision-making. The ability to process large volumes of data swiftly assists organizations in developing agile responses to market changes, thus fostering market growth.

Technological advancements and the increasing adoption of cloud computing are also contributing to market growth. Cloud-based in-memory data grids offer scalability, flexibility, and cost-efficiency, which are appealing to organizations seeking to optimize IT infrastructure. As more companies migrate to cloud environments, the demand for cloud-enabled data grids is expected to rise, driving further market expansion. Additionally, innovations in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with in-memory data grids, are enhancing grid capabilities, thus attracting greater interest from businesses looking to leverage these advanced technologies for enhanced data processing and analytics.

Regionally, North America is anticipated to maintain a dominant position in the in-memory data grids market due to the presence of major technology firms and high adoption rates of advanced technologies. The robust IT and telecommunications infrastructure in this region supports the widespread implementation of in-memory data grids. Meanwhile, Asia Pacific is projected to witness the highest growth rate, driven by rapid technological advancements, increasing investments in IT infrastructure, and growing awareness of data-driven decision-making. Europe is also expected to see significant growth, fueled by digital transformation initiatives and stringent data protection regulations that necessitate efficient data management solutions.

Component Analysis

In the realm of components, the in-memory data grids market is segmented into software and services. The software component is pivotal, as it encompasses the actual framework that facilitates data storage and retrieval within the grid. These software solutions are designed to enhance data processing capabilities, enabling organizations to manage and analyze vast datasets efficiently. With advancements in technology, software solutions have evolved to offer sophisticated features such as data replication, partitioning, and distributed caching, which are essential for ensuring data reliability and performance. The software segment is expected to hold a significant market share, driven by continuous innovation and the ongoing demand for high-performance data management solutions.

The services component of the in-memory data grids market plays a crucial role in supporting the implementation and optimization of grid solutions. This includes consulting, deployment, and support services that ensure seamless integration of in-memory data grids with existing IT infrastructures. As organizations increasingly adopt these solutions to enhance t

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When examining the intricate relationship between economic conditions and purchasing decisions, the utilization of practice datasets can offer invaluable insights. This particular artificial dataset comprises three main components: a dimension table of ten companies, a fact table documenting purchases from these companies, and a set of data points regarding economic conditions. These elements are meticulously designed to mimic real-world scenarios, enabling analysts to dissect and understand how fluctuations in the economy can influence the purchasing behavior of different types of companies.

The dimension table serves as the foundation, listing ten distinct companies, each potentially operating in varied sectors. This diversity allows for a comprehensive analysis across a spectrum of industries, highlighting sector-specific sensitivities to economic changes. The fact table of purchases acts as a historical record, offering detailed insights into the buying patterns of these companies over time. Analysts can observe trends, frequencies, and the magnitude of purchases, correlating them with the economic conditions presented in the third component of the dataset.

The economic conditions data is pivotal, as it encompasses a variety of indicators that can affect purchasing decisions. These may include inflation rates, interest rates, GDP growth, unemployment rates, and consumer confidence indices, among others. By examining the interplay between these economic indicators and the purchasing data, analysts can identify patterns and causations. For instance, an increase in interest rates might lead to a decrease in capital-intensive purchases by companies wary of higher borrowing costs.

Through this dataset, researchers can employ statistical models and data analysis techniques to uncover how economic fluctuations impact corporate purchasing decisions. The findings can offer valuable lessons for businesses in terms of budgeting, financial planning, and risk management. Companies can use these insights to make informed decisions, adjusting their purchasing strategies in anticipation of or in response to economic conditions. This proactive approach can help businesses maintain stability during economic downturns and capitalize on opportunities during favorable economic times.

Ultimately, this practice dataset not only aids in academic and educational pursuits but also serves as a practical tool for business analysts, economists, and corporate strategists seeking to better navigate the complex dynamics of the economy and its effects on corporate purchasing behaviors.

Iran IR: Lending Interest Rate data was reported at 18.000 % pa in 2016. This records an increase from the previous number of 14.210 % pa for 2015. Iran IR: Lending Interest Rate data is updated yearly, averaging 12.000 % pa from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 18.000 % pa in 2016 and a record low of 11.000 % pa in 2013. Iran IR: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Interest Rates. Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; ;

Nepal NP: Real Interest Rate data was reported at -6.207 % pa in 2010. This records an increase from the previous number of -6.823 % pa for 2009. Nepal NP: Real Interest Rate data is updated yearly, averaging 3.657 % pa from Dec 1975 (Median) to 2010, with 29 observations. The data reached an all-time high of 18.214 % pa in 1977 and a record low of -12.173 % pa in 1975. Nepal NP: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;