In a constantly changing world, the ability to anticipate and predict market trends has become an essential element for the survival and success of companies.
The use of Forecasting algorithms allows you to predict future trends through the analysis of historical data, resulting in a key tool for making informed strategic decisions and planning effective future actions based on data.
However, access to advanced forecasting and data science platforms can entail significant costs that weigh heavily on the budget of companies. In this article we will present the main free technologies that can be used to start to approach the world of time series.
The Importance of Forecasting and Artificial Intelligence
Forecasting is the key to anticipating future developments and ensuring sound planning. It relies on the analysis of historical data through statistical models and advanced algorithms to accurately and reliably estimate the future behavior of various parameters, such as sales, production, market demand and financial performance. In addition, it is possible to enrich the analysis by providing algorithms with data of external variables that can influence the course of the historical series. This allows companies to gain an in-depth view of market performance and make data–driven decisions to guide their business strategy.
Artificial Intelligence (AI) in recent years has become increasingly central in the context of forecasting: the application of machine and deep learning models allows to overcome several limitations of traditional statistical methods and allows to obtain very accurate and reliable forecasts.
What are Python and R?
Python and R are two widely used programming languages in the field of data analysis and machine learning. Python is a high-level programming language born in 1991, known for its simplicity, versatility and ease of reading code. Thanks to the wide range of specialized libraries such as Pandas, NumPye Scikit-learn, Python has become one of the preferred tools for data manipulation and analysis, allowing users to perform complex operations intuitively and efficiently. Tensorflow, Kerase Pytorch, on the other hand, are libraries that allow you to create neural networkse deep learning models. GluonTS , Orbite Prophetsono tra le principali librerie in Python per il forecasting.
On the other hand, R is a development environment specifically designed for statistical and graphical data analysis. This language offers a large collection of statistical packages, such as ggplot2 and dplyr, which simplify data analysis and visualization. R is particularly appreciated by the statistical and research community, thanks to its power in data processing and high quality graphics. For forecasting we mention the libraries forecast and auto.arima.
Both languages have their own strengths and are chosen according to the specific needs of the analysis. Python is often preferred for more general machine learning and data analysis projects, due to its flexibility and the vast community of developers while R shines for advanced statistical analysis and sophisticated graph creation, making it an ideal tool for academic research and specific data analysis. Both languages continue to gain popularity in the field of data analysis and complement each other in creating complex and innovative solutions to address the challenges of the data world.
Why to choose a free technology
The choice of technology depends on the specific needs of the company, the available resources, the complexity of the data and the projects to be implemented.
The main advantages of using free technologies include:
- Reduced costs: Eliminating fees for licensing paid forecasting platforms allows companies to significantly save on operating costs.
- Accessibility: companies of all sizes and with limited budgets can now benefit from an advanced forecasting solution, which was previously reserved for large companies.
- High precision: the machine learning and deep learning algorithms used in the various libraries are precise in predicting future trends and trends. Thanks to frequent updates, more modern and advanced models are soon available.
- Flexibility: technologies like Python and R provide a flexible and customizable environment for developing predictive models tailored to the specific needs of each company.
However, the use of free technology does not necessarily guarantee good computational performance, fast execution times and full support outside the community. In addition, a good knowledge of programming is required. These limits can be exceeded by adopting paid platforms.
Conclusion
Choosing to implement a forecasting project through free technologies allows you to contain costs, while having access to the most advanced and performing models. The use of machine learning and deep learning algorithms, together with programming languages such as Python and R, let you obtain excellent results, allowing companies to make more informed decisions and remain competitive in a constantly evolving market.
Blue BI has developed solid skills in forecasting and in the use of Python and R. BBI x Forecasting is the solution developed by Blue BI that takes advantage of the power of different technologies available and the statistical and machine learning skills of our team. With our forecasting solution, the future is truly at your fingertips for any business that wants to succeed!
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