By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

We use the yield curve to predict future GDP growth and recession probabilities. The spread between short- and long-term rates typically correlates with economic growth. Predications are calculated using a model developed by the Federal Reserve Bank of Cleveland. Released monthly.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Jun 2025 about recession indicators, academic data, and USA.

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

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.

NGT:TSX Stock: Are We Headed for a Recession?

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

United States Recession Probability data was reported at 14.120 % in Oct 2019. This records a decrease from the previous number of 14.505 % for Sep 2019. United States Recession Probability data is updated monthly, averaging 7.668 % from Jan 1960 (Median) to Oct 2019, with 718 observations. The data reached an all-time high of 95.405 % in Dec 1981 and a record low of 0.080 % in Sep 1983. United States Recession Probability data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

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.

LON:ETX Stock: Are We Headed for a Recession?

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

According to Cognitive Market Research, the global AI Sensor Market with Recession Market size is USD 2.8 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 38.6% from 2024 to 2031. Market Dynamics of AI Sensor Market with Recession Market

Key Drivers for AI Sensor Market with Recession Market

Advancements in AI and Machine Learning: Rapid advances in artificial intelligence and machine learning are boosting the use of Al sensors. Algorithms are getting increasingly sophisticated and capable of handling complicated data from sensors, enabling real-time decision-making and predictive analytics. These developments allow Al sensors to detect patterns, anomalies, and trends in data streams, making them useful in applications such as picture recognition, natural language processing, and predictive maintenance. For instance, in manufacturing, Al sensors may detect faults in real time, improving quality control and lowering waste. Al sensors also improve the capability of autonomous systems and robots. They can perceive their surroundings, adjust to changing circumstances, and make sound decisions. This is especially crucial in industries like agriculture, where autonomous drones equipped with Al sensors can check crop health, detect pest infestations, and optimize pesticide use. Security and Surveillance applications

Key Restraints for AI Sensor Market with Recession Market

Capital Spending Delays in Price-Sensitive Sectors: Businesses in a variety of sectors, including retail, consumer electronics, and the automobile industry, frequently postpone or abandon capital-intensive initiatives and technological advancements during recessions. This has a direct impact on the use of AI sensors in consumer electronics, smart factories, and new goods, momentarily reducing market expansion.

Semiconductor shortages and supply chain disruptions: Complex semiconductor components are necessary for AI sensors, and supply chain bottlenecks are frequently made worse by global economic downturns. Delays in shipping, reduced manufacturing capacity, and geopolitical unrest can all affect sensor production and lengthen lead times, making it more difficult for industries to deploy sensors on time.

Key Trends for AI Sensor Market with Recession Market

Transition to Low-Cost Advanced AI Sensors: Industries are turning to edge AI sensors that analyze data locally in order to deal with financial restrictions. This eliminates the need for expensive cloud infrastructure and latency problems. Due to their simplicity of deployment and reduced total cost of ownership, small, energy-efficient sensors with on-chip AI are becoming more and more popular. Growing Utilization in Energy Efficiency and Predictive Maintenance: Operational efficiency is a top priority for financially stressed organizations, and AI sensors are essential for energy optimization and predictive maintenance. Industrial equipment with sensors built in can anticipate malfunctions, prolong the life of machinery, and use less electricity, all of which can result in quantifiable cost savings during recessions. Introduction of the AI Sensor Market with Recession Market

Al sensors are also improving the capabilities of autonomous systems and robots. They can perceive their surroundings, adjust to changing conditions, and make sound decisions. This is especially crucial in industries like agriculture, where autonomous drones equipped with Al sensors can check crop health, detect pest infestations, and optimize pesticide use. Also, increased demand for life-saving healthcare equipment and self-driving capabilities in new electric vehicles are expected to fuel growth. The global shift towards digitization is expected to boost growth even further.

The 2020 recession did not follow the trend of previous recessions in the United States because only six months elapsed between the yield curve inversion and the 2020 recession. Over the last five decades, 12 months, on average, has elapsed between the initial yield curve inversion and the beginning of a recession in the United States. For instance, the yield curve inverted initially in January 2006, which was 22 months before the start of the 2008 recession. A yield curve inversion refers to the event where short-term Treasury bonds, such as one or three month bonds, have higher yields than longer term bonds, such as three or five year bonds. This is unusual, because long-term investments typically have higher yields than short-term ones in order to reward investors for taking on the extra risk of longer term investments. Monthly updates on the Treasury yield curve can be seen here.

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.

DTRTU Stock: Are We Headed for a Recession?

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

United States Recession Prob: Yield Curve: 3 Month Treasury Yield data was reported at 2.250 % in Oct 2018. This records an increase from the previous number of 2.130 % for Sep 2018. United States Recession Prob: Yield Curve: 3 Month Treasury Yield data is updated monthly, averaging 4.620 % from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 16.300 % in May 1981 and a record low of 0.010 % in Dec 2011. United States Recession Prob: Yield Curve: 3 Month Treasury Yield data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

United States banking professionals believed in Q2 2022 that a Fed overcorrection was a probable cause for a recession. 51 percent of the respondents believed that the too fast and too highly increasing Fed rates would result in an economic recession. 25 percent of the respondents predicted that a recession would occur because of supply chain problems, while five percent mentioned the conflict in Eastern Europe as the main cause for a possible recession.

This paper proposes a model to predict recessions that accounts for non-linearity and a structural break when the spread between long- and short-term interest rates is the leading indicator. Estimation and model selection procedures allow us to estimate and identify time-varying non-linearity in a VAR. The structural break threshold VAR (SBTVAR) predicts better the timing of recessions than models with constant threshold or with only a break. Using real-time data, the SBTVAR with spread as leading indicator is able to anticipate correctly the timing of the 2001 recession.

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.

if the stock market goes down during a recession, you should sell all of your investments to minimize your losses.

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

Probability of Recession: United States data was reported at 0.995 % in Mar 2025. This records a decrease from the previous number of 1.031 % for Feb 2025. Probability of Recession: United States data is updated monthly, averaging 1.564 % from Jan 1980 (Median) to Mar 2025, with 543 observations. The data reached an all-time high of 87.972 % in May 2020 and a record low of 0.021 % in Jan 1980. Probability of Recession: United States data remains active status in CEIC and is reported by CEIC Data. The data is categorized under World Trend Plus’s CEIC Leading Indicator – Table US.S002: Probability of Recession.

According to projections by a range of economic institutions, the economy of the Euro currency area is forecast to grow by between 0.5 percent and 1.2 percent in 2024. The Eurozone saw slow growth in 2023, when it grew by 0.7 percent - albeit this was significantly better than many economic forecasts which predicted a recession in the EU in that year. Across all the forecasts included, growth is expected to pick up in 2025, when the Eurozone's economy is expected to grow between 1.4 and 1.8 percent.

This repository hosts the supplementary materials associated with the paper:
> Delforge, D., Muñoz-Carpena, R., Van Camp, M. Vanclooster, M. (2020), A parsimonious empirical approach to streamflow recession analysis and forecasting (accepted at Water Resources Research - 29-01-2020).

This data set contains streamflow and recession data, a python code file and a Jupyter notebook illustrating how to apply the EDM-Simplex method to forecast the recession, and the outputs of the global sensitivity analysis. All files are documented in the readme.md Markdown files.

Streamflow data were obtained from the Aqualim portal (http://aqualim.environnement.wallonie.be/) of the "Service Public de Wallonie" and shared with their kind permission. This work is part of a Ph.D. supported by a FRIA grant from the Fund for Scientific Research (FSR-FNRS, Belgium). The authors acknowledge University of Florida Research Computing for providing computational resources and support that have contributed to the research results stored in this repository. URL: http://researchcomputing.ufl.edu.

This metadata record describes observed and predicted baseflow recession characteristics for 300 streamflow gauges in the western United States and 282 streamflow gauges in the eastern United States. Specifically, this record describes (1) the streamflow gauge locations (west or east) in the United States (Location), (2) the U.S. Geological Survey streamflow gauge identification numbers (USGS_Site_Identifier), (3) observed regions of similar aquifer hydraulic properties (7 regions coded by color: blue, green, red, purple, grey, pink, and orange) by k-means clustering method (Observed_Class(k-means)), (4) predicted regions of similar aquifer hydraulic properties by random forest classification models (Predicted_Class(k-means)), (5) calculated long-term baseflow recession constant at streamflow gauges (Observed_a-long[ft^(-3/2)s^(-1/2)]), (6) predicted long-term baseflow recession constant by novel empirical and physical approach (Predicted_a-long(Novel)[ft^(-3/2)s^(-1/2)]), (7) pre ...

In 2024, the gross domestic product (GDP) of the United Kingdom grew by *** percent and is expected to grow by just *** percent in 2025 and by *** percent in 2026. Growth is expected to slow down to *** percent in 2027, and then grow by ***, and *** percent in 2027 and 2028 respectively. The sudden emergence of COVID-19 in 2020 and subsequent closure of large parts of the economy were the cause of the huge *** percent contraction in 2020, with the economy recovering somewhat in 2021, when the economy grew by *** percent. UK growth downgraded in 2025 Although the economy is still expected to grow in 2025, the *** percent growth anticipated in this forecast has been halved from *** percent in October 2024. Increased geopolitical uncertainty as well as the impact of American tariffs on the global economy are some of the main reasons for this mark down. The UK's inflation rate for 2025 has also been revised, with an annual rate of *** percent predicated, up from *** percent in the last forecast. Unemployment is also anticipated to be higher than initially thought, with the annual unemployment rate likely to be *** percent instead of *** percent. Long-term growth problems In the last two quarters of 2023, the UK economy shrank by *** percent in Q3 and by *** percent in Q4, plunging the UK into recession for the first time since the COVID-19 pandemic. Even before that last recession, however, the UK economy has been struggling with weak growth. Although growth since the pandemic has been noticeably sluggish, there has been a clear long-term trend of declining growth rates. The economy has consistently been seen as one of the most important issues to people in Britain, ahead of health, immigration and the environment. Achieving strong levels of economic growth is one of the main aims of the Labour government elected in 2024, although after almost one year in power it has so far proven elusive.

The global gum recession line market is estimated to be valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period from 2025 to 2033. The market is driven by the increasing prevalence of periodontal diseases, such as gingivitis and periodontitis, which are major causes of gum recession. Additionally, rising awareness about oral hygiene and the growing adoption of minimally invasive dental procedures are fueling market growth. The availability of advanced techniques, such as laser therapy and guided tissue regeneration, is also contributing to the market expansion. The gum recession line market is segmented based on application, type, and region. By application, the market is divided into hospitals, dental clinics, and others. By type, the market is categorized into braided cords, knitted cords, twisted cords, and others. Geographically, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America is expected to dominate the global market throughout the forecast period due to the high prevalence of periodontal diseases and the adoption of advanced dental care technologies. Europe is also a major market for gum recession lines, followed by Asia Pacific. This report provides an in-depth analysis of the Gum Recession Line market, focusing on concentration, trends, key regions, product insights, and drivers. The market is valued at $XX billion in 2023 and is projected to grow to $XX billion by the end of 2032, exhibiting a CAGR of XX% during the forecast period.