Master time series forecasting with PyTorch. Build and optimize RNNs, LSTMs, and GRUs for univariate and multivariate data. Advance from basic sequences to complex hybrid models and attention mechanisms to solve real-world data science challenges.
Learn to build and evaluate RNN, LSTM, and GRU models for time series forecasting using PyTorch. You'll work with univariate and multivariate data, implement classification tasks, and apply advanced techniques to boost forecasting performance.
Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal
From our community
Hear what our customers have to say about CodeSignal Learn
I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!
Francisco Aguilar Meléndez
Data Scientist
+11
I love that it's personalized. When I'm stuck, I don't have to hope my Google searches come out successful. The AI mentor Cosmo knows exactly what I need.
Faith Yim
Software Engineer
+14
It's an amazing product and exceeded my expectations, helping me prepare for my job interviews. Hands-on learning requires you to actually know what you are doing.
Alex Bush
Full Stack Engineer
+9
I'm really impressed by the AI tutor Cosmo's feedback about my code. It's honestly kind of insane to me that it's so targeted and specific.
Abbey Helterbran
Tech consultant
+8
I tried Leetcode but it was too disorganized. CodeSignal covers all the topics I'm interested in and is way more structured.
Jonathan Miller
Senior Machine Learning Engineer
+12
I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!
Francisco Aguilar Meléndez
Data Scientist
+11
13 practices
This course extends the concepts from the first course by introducing multiple time series inputs. It covers how to preprocess, structure, and train RNN models using **two related time series features** from the **Air Quality dataset**. It also includes model evaluation techniques to assess forecasting accuracy.
This course focuses on LSTM networks, a powerful extension of RNNs that handle long-term dependencies better than simple RNNs. Learners will build, optimize, and evaluate LSTM models for time series forecasting.
This course explores Gated Recurrent Units (GRUs) in PyTorch for multivariate time series forecasting. We will build, evaluate, and apply advanced GRU techniques like Bidirectional GRUs, Attention Mechanisms, and Hybrid GRU-CNN models to improve forecasting accuracy.
