Displayed is the annual revenue of Celsius Holdings from 2017 to 2024. In 2017, the company's total yearly gross sales equated to nearly ** million U.S. dollars. Since then, there has been a steady increase in yields. Celsius Holdings' revenue grew exponentially in 2020, to about *** million U.S. dollars. Earnings for the company continued to climb in 2024, peaking at almost **** billion U.S. dollars. Celsius Holdings Inc. is located in the United States. Founded in 2004, the company is involved with developing and distributing functional drinks and liquid supplements, such as CELSUIS®.

Displayed are the net incomes of Celsius Holdings from 2018 to 2024. The company's net income has decreased year-to-year since 2018. In 2024, Celsius Holdings net losses were about ****million U.S. dollars. Celsius Holdings Inc. is located in the United States. The company was founded in 2004 and is involved with developing and distributing functional drinks and supplements.

In 2024, the region with the highest reported revenue of Celsius Holdings was North America, with around **** billion U.S. dollars. In contrast, Asia amounted to nearly *** million U.S. dollars in revenue. The main contributor to Celsius Holdings' regional Asian revenue is generated from their Chinese license. Celsius Holdings Inc., is located in the United States. Founded in 2004, the company is involved with developing and distributing functional drinks and liquid supplements, such as CELSIUS®.

Shown are the gross profits of Celsius Holdings from 2017 to 2024. The company's gross profits have increased year-to-year since 2017. In 2024, gross profits for Celsius Holdings soared to about *** million U.S. dollars. Celsius Holdings Inc. is located in the United States. Founded in 2004, the company is involved with developing and distributing functional drinks and liquid supplements, such as CELSIUS®.

Capital-Expenditures Time Series for Celsius Holdings Inc. Celsius Holdings, Inc. develops, processes, manufactures, markets, sells, and distributes functional energy drinks in the United States, North America, Europe, the Asia Pacific, and internationally. The company offers CELSIUS, a lifestyle and energy drink; CELSIUS Originals and Vibe, which are functional energy drinks in various flavors that is offered in carbonated and non-carbonated forms; CELSIUS ESSENTIALS, a functional energy drink formulated with aminos; CELSIUS On-the-Go Powder, a functional energy drink in powder form; and CELSIUS Hydration, a line of zero-sugar hydration powders infused electrolytes in various fruit-forward flavors, as well as CELSIUS ready-to-drink products. It distributes its products through direct-to-store delivery, independent distributors, supermarkets, convenience stores, drug stores, nutritional stores, food service providers, and mass merchants, as well as natural food stores, fitness centers, mass market retailers, vitamin specialty stores, club stores, gyms, and e-commerce platforms. The company was formerly known as Vector Ventures, Inc. and changed its name to Celsius Holdings, Inc. in January 2007. Celsius Holdings, Inc. was founded in 2004 and is headquartered in Boca Raton, Florida.

Traffic analytics, rankings, and competitive metrics for celsius.network as of June 2025

Celsius Holdings stock jumps 21% following strong Q2 earnings, with 84% revenue growth and expanding market share in the energy drink industry.

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

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• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

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• LandMaxTemperature: global average maximum land temperature in celsius

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• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

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• LandMinTemperature: global average minimum land temperature in celsius

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• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

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• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

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• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

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**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

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• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

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• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

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• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

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The raw data comes from the Berkeley Earth data page.

Celsius Holdings defied a 4.4% sales decline to surpass Wall Street's Q4 CY2024 revenue estimates, demonstrating strong growth potential and strategic foresight.

Evaluating multiple signals of climate change across the conterminous United States during three 30-year periods (2010�2039, 2040�2069, 2070�2099) during this century to a baseline period (1980�2009) emphasizes potential changes for growing degree days (GDD), plant hardiness zones (PHZ), and heat zones. These indices were derived using the CCSM4 and GFDL CM3 models under the representative concentration pathways 4.5 and 8.5, respectively, and included in Matthews et al. (2018). Daily temperature was downscaled by Maurer et al. (https://doi.org/10.1029/2007EO470006) at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones. Each of these indices provides unique information about plant health related to changes in climatic conditions that influence establishment, growth, and survival. These data and the calculated changes are provided as 14 individual IMG files for each index to assist with management planning and decision making into the future. For each of the four indices the following are included: two baseline files (1980�2009), three files representing 30-year periods for the scenario CCSM4 under RCP 4.5 along with three files of changes, and three files representing 30-year periods for the scenario GFDL CM3 under RCP 8.5 along with three files of changes.Heat zones map the distribution of potential heat stress for plants and animals, including humans. We define heat zones as the number of days with maximum daily temperature >30 �C (86 �F). Because species have unique adaptations and abilities to tolerate a wide variety of conditions, this metric is used merely as an indicator of change in �hot� conditions. The 30 �C value is set primarily for agricultural production and is a general temperature threshold at which photosynthesis can be negatively impacted for C3 plants (e.g., most species including trees), but it certainly also captures temperatures that induce stress in humans as well. In addition, increases in temperature above these thresholds for longer periods, especially when accompanied with prolonged dry conditions, are linked to reduced performance and likely mortality of trees. Each day surpassing the 30 �C threshold was tallied and summed for each year and reported as the mean number of days, per year, over each 30-year period: baseline, early, mid, and late century.�Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001

This dataset contains the predicted prices of Celsius for the upcoming years based on user-defined projections.

Vietnam Mean Air Temperature: Pleiku data was reported at 22.800 Degrees Celsius in 2017. This records a decrease from the previous number of 23.300 Degrees Celsius for 2016. Vietnam Mean Air Temperature: Pleiku data is updated yearly, averaging 22.350 Degrees Celsius from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 23.300 Degrees Celsius in 2016 and a record low of 21.600 Degrees Celsius in 2011. Vietnam Mean Air Temperature: Pleiku data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.RI015: Weather Statistics: Sunshine, Rainfall, Mean Humidity, Mean Air Temperature.

Mean January temperatures for land areas of the world in degrees Celsius. Fahrenheit and Celsius values are included in pop-ups. Data derived from WorldClim Global Climate Data daily January average temperatures from 1950-2000.

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.

Is Celsius (CELH) Stock Poised for a Hot Run?

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

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Celsius Network on 2025-09-28. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

PepsiCo strengthens its position in the energy drink market with a $585 million deal to increase its stake in Celsius to 11% and names Celsius its strategic energy lead in the US and Canada.

Baseline (1961-1990) average winter temperature in and projected change in temperature for for the northern portion of Alaska. For the purposes of these maps, 'winter' is defined as December - February. The Alaska portion of the Arctic LCC's terrestrial boundary is depicted by the black line. Baseline results for 1961-1990 are derived from Climate Research Unit (CRU) TS3.1 data and downscaled to 2km grids; results for the other time periods (2010-2039, 2040-2069, 2070-2099) are based on the SNAP 5-GCM composite using the AR5-RCP 8.5, downscaled to 2km grids.

Romania Heating Degree Days data was reported at 5,357.060 Degrees Celsius in 2020. This records an increase from the previous number of 5,202.560 Degrees Celsius for 2019. Romania Heating Degree Days data is updated yearly, averaging 6,233.850 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 7,055.300 Degrees Celsius in 1985 and a record low of 5,202.560 Degrees Celsius in 2019. Romania Heating Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank.WDI: Environmental: Climate Risk. A heating degree day (HDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is below 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

The total assets of Celsius Holdings with headquarters in the United States amounted to *** billion U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately *** billion U.S. dollars. The trend from 2020 to 2024 shows, furthermore, that this increase happened continuously.

These tabular data sets represent daily climate metrics processed from 4 kilometer snow water equivalent (SWE) raster data in millimeters (Broxton and others, 2019) for the period of record 10-01-1981 through 09-30-2020 and compiled for three spatial components: select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper and Lower Colorado (ucol) portions of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values were computed using the published python software package Xstrm (Wieferich and others).