The dataset contains full time series of satellite and radar images,
weather models and ground observations.
To keep the dataset at a reasonable size, the data covers two geographic
areas of 550km x 550km on the Mediterranean and Brittany coasts, and spans
over 3 years, 2016 to 2018.
We have prepared this free dataset to let the data science community play with it.
Explore it today!
Brian, a renowned technical analyst, has developed a unique approach to using multiple timeframes. According to Brian, "The key to successful trading is to have a multi-faceted view of the market. By analyzing multiple timeframes, traders can gain a more complete understanding of the underlying trends and patterns that are driving the market."
Brian's approach involves using a combination of short-term and longer-term timeframes to identify trading opportunities. He emphasizes the importance of flexibility and adaptability, and encourages traders to adjust their timeframes and technical indicators based on changing market conditions.
Technical analysis is a type of investment analysis that focuses on studying price charts and other market data to make predictions about future price movements. It is based on the idea that market prices reflect all available information, and that by analyzing price charts and trends, traders can identify patterns and make informed investment decisions. Technical Analysis Using Multiple Timeframes By Brian
Using multiple timeframes can provide a more comprehensive view of a security's price movements. By analyzing a security's price movements on different timeframes, traders can gain a better understanding of the underlying trends and patterns that are driving the market. This can help traders make more informed investment decisions and improve their trading performance.
Technical analysis is a method of evaluating securities by analyzing statistical patterns and trends in their price movements. One of the most effective ways to apply technical analysis is by using multiple timeframes. In this article, we will explore the concept of technical analysis using multiple timeframes, and how it can be used to improve your trading decisions. Brian, a renowned technical analyst, has developed a
Multiple timeframes refer to the use of different time intervals to analyze a security's price movements. For example, a trader may use a short-term timeframe, such as a 5-minute chart, to identify short-term trends and patterns, and a longer-term timeframe, such as a daily chart, to identify longer-term trends and patterns.
Let's say that you are analyzing the price movements of a stock using multiple timeframes. On the 5-minute chart, you notice that the stock is trending upwards, with a series of higher highs and higher lows. On the 30-minute chart, you notice that the stock is consolidating, with a range-bound price movement. On the daily chart, you notice that the stock is in a long-term uptrend, with a series of higher highs and higher lows. Using multiple timeframes can provide a more comprehensive
By comparing the trends and patterns on the different timeframes, you can gain a more comprehensive view of the market. You may decide to buy the stock on the 5-minute chart, but be cautious of the consolidation on the 30-minute chart. You may also decide to hold onto the stock for the long-term, based on the uptrend on the daily chart.
Technical analysis using multiple timeframes is a powerful tool for traders and investors. By analyzing a security's price movements on different time intervals, traders can gain a more comprehensive view of the market and make more informed investment decisions. By using multiple timeframes, traders can improve their accuracy, reduce noise, and enhance their trading performance.
Have a look at our toolbox which includes data samples from MeteoNet written in python language and our tutorials/documentation which help you explore and cross-check all data types.

Play with it and if you send us your results, we could showcase them on this website!
Download MeteoNetThe data are also available on Kaggle with notebooks to help you explore and cross-check all data types!
You can contribute to challenges and/or propose yours!
Time series prediction
Rainfall nowcasting
Cloud cover nowcasting
Observation data correction
...etc
You did something interesting with our
dataset? Want your project to be showcased here?
Write a blog, contact us on GitHub, and we will come back to you!
Need help? Checkout our documentation, post an issue on our GitHub repository or go to our Slack workspace!
Documentation GitHub SlackYou can find other data on METEO FRANCE public data website. It features real-time, past and forecast data: in situ observations, radar observations, numerical weather models, climate data, climate forecasts and much more!
The Dataset is licenced by METEO FRANCE under Etalab Open Licence 2.0.
Reuse of the dataset is free, subject to an acknowledgement of authorship. For example:
"METEO FRANCE - Original data downloaded from https://meteonet.umr-cnrm.fr/, updated on 30 January 2020".
When using this dataset in a publication, please cite:
Gwennaëlle Larvor, Léa Berthomier, Vincent Chabot, Brice Le Pape, Bruno Pradel, Lior Perez. MeteoNet, an open reference weather dataset by METEO FRANCE, 2020