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.

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

View monthly updates and historical trends for US Recession Probability. from United States. Source: Federal Reserve Bank of New York. Track economic data…

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

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

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.

A new approach to recession forecasting outperforms competing methods up to 12 months in advance.

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

A recession is due in the U.S. in 2023, according to a majority of macroeconomists in a June 2022 survey. Opinions varied, however, on when in 2023 this new recession could start exactly. Most respondents - ** percent - believed the economic downturn most likely start in the first half of 2023. Meanwhile, ** percent said that it would begin in the latter half of that year. Most Americans thought differently on this topic, believing that the country was already experiencing an economic recession in June 2022. The macroeconomists cited both geopolitical tensions and the increasing costs of energy as the main reasons why pressure would remain on U.S. inflation.

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.

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.

The AI Sensor market is poised for explosive growth, demonstrating remarkable resilience even amidst a global recession. Driven by the urgent need for automation, efficiency, and cost optimization across industries, the demand for intelligent sensors is accelerating. While economic uncertainty may cause short-term hesitations in capital expenditure, the long-term strategic value of AI-driven data analysis in predictive maintenance, quality control, and autonomous systems positions the market for substantial expansion. Sectors such as manufacturing, automotive, healthcare, and logistics are leading this adoption wave. The market's trajectory is fueled by advancements in edge computing, IoT proliferation, and increasingly sophisticated machine learning algorithms, which together unlock unprecedented operational insights and capabilities, making AI sensors a critical investment for future-proofing businesses. Key strategic insights from our comprehensive analysis reveal:

Despite recessionary pressures, the market is projected to grow at an exceptional CAGR of 38.6%, as businesses prioritize long-term efficiency and automation investments over short-term discretionary spending.
The push for operational resilience is shifting focus towards high-ROI applications like predictive maintenance and energy management, which offer clear and rapid cost-saving benefits in a challenging economic climate.
North America and Asia Pacific are the dominant regions, driven by strong technology ecosystems and massive manufacturing bases, respectively, creating a competitive and innovative landscape for AI sensor development and deployment.

Global Market Overview & Dynamics of AI Sensor Market with Recession Market Analysis The global AI Sensor market is on a path of transformative growth, fundamentally reshaping how industries collect, process, and act on data. This expansion is propelled by the convergence of advanced sensor technology, powerful edge computing, and sophisticated AI algorithms. Even with the backdrop of a global recession, the market's momentum is sustained by an intensified focus on automation and operational efficiency as companies seek to reduce costs and enhance productivity. AI sensors are becoming integral to diverse applications, from industrial IoT and autonomous vehicles to smart cities and personalized healthcare, creating a dynamic and highly competitive environment. The ability of these sensors to provide real-time, actionable intelligence at the source is the core value proposition driving their widespread adoption. Global AI Sensor Market with Recession Market Drivers

Imperative for Automation and Cost Reduction: During a recession, businesses aggressively seek to reduce operational expenditures and enhance productivity. AI sensors enable automation in manufacturing, logistics, and quality control, directly addressing these needs by minimizing labor costs, reducing errors, and optimizing resource utilization.
Proliferation of IoT and Edge Computing: The expanding Internet of Things (IoT) ecosystem generates massive volumes of data. AI sensors with edge computing capabilities can process this data locally, reducing latency, lowering bandwidth costs, and enabling real-time decision-making, which is critical for applications like autonomous systems and smart infrastructure.
Advancements in AI and Sensor Technology: Continuous improvements in machine learning algorithms, coupled with the miniaturization and cost reduction of high-performance sensors (like LiDAR, radar, and image sensors), are making sophisticated AI-powered sensing more accessible and effective for a broader range of applications.

Global AI Sensor Market with Recession Market Trends

Surge in Predictive Maintenance Applications: Industries are increasingly adopting AI sensors to monitor equipment health in real-time. By predicting failures before they occur, companies can minimize costly unplanned downtime and transition from reactive to proactive maintenance strategies, a trend that gains significant traction during economic downturns.
Integration into Autonomous Vehicles and ADAS: The automotive sector is a key growth area, with AI sensors forming the sensory backbone of Advanced Driver-Assistance Systems (ADAS) and fully autonomous vehicles. The fusion of data from cameras, radar, and LiDAR, processed by onboard AI, is critical for safe and reliable navigation.
Rise of TinyML and On-Device AI: The trend ...

In 2019, ** percent of American respondents said that President Trump would be the most responsible if the United States were to enter into a recession. This is compared to **** percent of respondents, who said that former President Barack Obama would be the most responsible.

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

In the event of a global recession and in the wake of the coronavirus outbreak, forecasts indicate that the growth of Luxembourg's economy would slow by over *** percent in 2020. During this time, GDP in the Grand Duchy showed a *** percent growth relative to 2018. In **********, however, stock markets suffered heavy losses as governments worldwide tried to contain the pandemic and fears grew on the economic consequences of this virus.

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.

United States FRB Recession Risk data was reported at 0.178 % in Apr 2025. This records a decrease from the previous number of 0.192 % for Mar 2025. United States FRB Recession Risk data is updated monthly, averaging 0.193 % from Jan 1973 (Median) to Apr 2025, with 628 observations. The data reached an all-time high of 1.000 % in Oct 2008 and a record low of 0.022 % in Jul 2003. United States FRB Recession Risk data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S090: FRB Recession Risk.

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.