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Time series Forecasting of Wind speed based on different deep learning methods LSTM - GRU

Different deep learning architectures are implemented for time series classification and prediction purposes.

This content shows how to implement time series models provided by Deep Learning Toolbox and Econometrics Toolbox on Simulink model and to update them and forecast value at next time step recursively.

Designing a Machine Learning algorithm to predict stock prices is a subject of interest for economists and machine learning practitioners. Financial modelling is a challenging task, not only from an analytical perspective but also from a psychological perspective. After 2008 financial crisis, man... Read more

Using Reinforcement Learning on S&P500 dataset to predict the future stock prices. The implementation uses deep Q-learning model along with time series modeling to achieve the goal state.

2020 was a roller coaster of major, world-shaking events. We all couldn't wait for the year to end. But just as 2020 was about to close, it pulled another fast one on us: the SolarWinds hack, one of the biggest cybersecurity breaches of the 21st century. The SolarWinds hack was a major event not... Read more

The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data. The first way is using continuous wavelet transform and transfer learning, whereas the second way is using Wavelet Scattering and LSTMs. The explanations of the code are in Chinese. The used dat... Read more

Time Series Analysis with Deep Learning.

Repository for Machine Learning and Deep Learning Models for Multivariate Time Series Forecasting

"In "Time Series Analysis for Finance in Python", we navigate the complex rhythms and patterns of financial data, diving deep into how time series analysis plays a pivotal role in understanding and predicting the dynamics of financial markets."