• Descriptive statistics
• ARMA
• State Space
• Bayesian inferance, etc.

Non-NN Machine Learning Methods

• Supervised learning algorithms, such as decision trees, linear regression, logistic regression, support vector machines.
• Unsupervised learning algorithms, for example, K-means clustering and hierarchical clustering.
• Reinforcement learning methods, such as Q-learning, SARSA, temporal differences method.

Neural Networks, Including Deep Learning

• Convolutional and recurrent neural networks (including LSTM and GRU)
• Autoencoders
• Generative adversarial networks (GANs)
• Deep Q-network (DQN)
• Bayesian deep learning

• -In all our work, only open-source software are used in the operating system, virtualization, data processing, visualization, modeling, and reporting stages.
• Open-source software, unlike closed source software, has the advantages of adding new plugins, modules, and features by many volunteer developers, fast debugging, and fast new version cycle.
• In addition, much wider documentation and support information is available in open-source software, and the information obtained in these tools is used in different application areas.
• The open-source services industry exceeded $17 billion in 2019 and is expected to reach approximately $33 billion by 2022 (CB Insights).
• More than 30 million developers contribute through platforms like GitHub.
• The TIOBE index is an indicator of the popularity of programming languages. The index is updated monthly. Ratings are based on the number of qualified engineers, training programs and suppliers worldwide.
• The TIOBE Index for June 2020:
• 

Defining the problem correctly, including the right variables in the model, defining new variables, making the necessary transformations are the most important elements that distinguish us from other companies.