First launched in 2010, PlayStation Plus is Sony’s subscription service which allows PlayStation users to play online multiplayer. As of March 2023, the service had over 47.4 million subscribers, a decrease from its peak of 48 million subscriptions in December 2021. In June 2022, Sony combined its PlayStation Plus and PlayStation Now cloud gaming services and the revamped PlayStation Plus service now features a three-tier subscription model. The existing service became PlayStation Plus Essential, with higher priced tiers PlayStation Plus Extra and PlayStation Plus Premium adding access to downloadable and streamable gaming titles which were formerly available under PlayStation Now.

PlayStation Plus perks As well as enabling gamers to play with their friends online, PlayStation Plus comes with several other perks. PS Plus members are given early access to upcoming games and are given discounts on PlayStation Store items. One of the biggest perks is the gift of two PlayStation 4 and one PS5 game every month. These so-called Instant Game Collection games have ranged from blockbuster games such as Call of Duty: Black Ops 4 (PS4) and A Plague Tale: Innocence (PS5), to smaller games such as a limousine driving game, Roundabout, and Knowledge is Power, a local multiplayer quiz game. In total, PlayStation Plus Essential subscribers were offered 1,304 U.S. dollars’ worth of free video games in 2022.

 PlayStation still going strong Announced in 2019, Sony's PlayStation 5 was released in November 2020. The PS5, along with Microsoft's Xbox Series S and Series X consoles, which were launched in the same month, are part of the ninth generation of video game consoles. Industry estimates that the PS5 will generate over 67 million unit sales by 2024. However, the rollout of the flagship console was marred by limited supply and scalpers taking advantage of the situation, driving up the price of the console for thousands of dollars in the secondary market.

The MOD17A2HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD17A2HGF Version 6.1 data product.The MOD17A2HGF Version 6 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year.Known Issues Operational and uncertainty issues are provided under Section 2 in the User Guide. For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.Improvements/Changes from Previous Versions The product uses updated Biome Property look-up tables (BPLUT) and an updated version of the daily Global Modeling and Assimilation Office (GMAO) meteorological data. The products are now generated at the native resolution of 500 m using the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index(LAI)/Fraction of Photosynthetically Active Radiation (FPAR) (MOD15A2H) 8-day composite at 500 m resolution.

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.

PSN Parsons Corporation Common Stock

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

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.

Should You Buy Now or Wait? PSN Stock Forecast

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 MOD17A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD17A3HGF Version 6.1 data product.The MOD17A3H Version 6 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR).Known Issues Forward processing of MOD17A3H Version 6 data product was discontinued on January 3, 2004, due to unexpected errors in the input data. Users are encouraged to use the MOD17A3HGF Version 6.1 data product. For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.Improvements/Changes from Previous Versions The product uses updated Biome Property look-up tables (BPLUT) and an updated version of the daily Global Modeling and Assimilation Office (GMAO) meteorological data. The products are now generated at the native resolution of 500 m using the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI)/Fraction of Photosynthetically Active Radiation (FPAR) (MOD15A2H) 8-day composite at 500 m resolution.

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.

Will PSN Stock Recapture Lost Ground in 2023?

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 MOD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Terra Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR).The MOD17A3HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H is available. Hence, the gap-filled MOD17A3HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A3HGF in near-real time because it will be generated only at the end of a given year.Known Issues Operational and uncertainty issues are provided under Section 2 in the User Guide. For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.Improvements/Changes from Previous Versions The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.