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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.
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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.
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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?
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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.
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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]
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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.
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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.
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Senegal SN: Interest Rate Spread data was reported at -1.630 % pa in 2016. This records an increase from the previous number of -1.844 % pa for 2015. Senegal SN: Interest Rate Spread data is updated yearly, averaging -3.008 % pa from Dec 2005 (Median) to 2016, with 12 observations. The data reached an all-time high of -0.558 % pa in 2005 and a record low of -3.602 % pa in 2009. Senegal SN: Interest Rate Spread data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Senegal – Table SN.World Bank.WDI: Interest Rates. Interest rate spread is the interest rate charged by banks on loans to private sector customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; Median;
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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.; ;
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Iraq IQ: Real Interest Rate data was reported at 53.543 % pa in 2015. This records an increase from the previous number of 13.392 % pa for 2014. Iraq IQ: Real Interest Rate data is updated yearly, averaging 0.677 % pa from Dec 2004 (Median) to 2015, with 12 observations. The data reached an all-time high of 53.543 % pa in 2015 and a record low of -14.278 % pa in 2005. Iraq IQ: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iraq – Table IQ.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.; ;
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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.
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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.; ;
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.
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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 ...).
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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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.
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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JP: Short-Term Interest Rate data was reported at 1.321 % in 2026. This records an increase from the previous number of 0.821 % for 2025. JP: Short-Term Interest Rate data is updated yearly, averaging 0.836 % from Dec 1969 (Median) to 2026, with 58 observations. The data reached an all-time high of 14.512 % in 1974 and a record low of 0.060 % in 2017. JP: Short-Term Interest Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.EO: Interest Rate: Forecast: OECD Member: Annual. IRS - Short-term interest rate; Japan interbank 3 mth (LDN:BBA) - offered rate. Available from 1986 onwards.
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The benchmark interest rate in Indonesia was last recorded at 5.50 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Fixed 30-year mortgage rates in the United States averaged 6.77 percent in the week ending July 4 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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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.