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🎲 Probabilistic Time Series Forecasting with JAX – Embracing UncertaintySo far, we’ve built deterministic models: they output a single forecast.But in the real world, uncertainty matters.👉 Think about:Energy demand forecasting → confidence intervals matter for grid planningStock predictions → risk management needs probabilistic rangesWeather forecasting → nobody trusts a single numberThat’s wh..

🚀 End-to-End Time Series Forecasting Pipeline with JAXSo far, we’ve explored:JAX basics, vectorization, and GPU accelerationRNNs, GRUs, and Transformers for forecastingTemporal Fusion Transformers (TFT) for interpretabilityNow it’s time to connect everything into a production-style pipeline.🏗 Step 1: Data PreprocessingWe’ll use the electricity consumption dataset (or any CSV with a date + valu..

🧭 Interpretable Forecasting with Temporal Fusion Transformer (TFT) in JAXForecasting models are often black boxes.The Temporal Fusion Transformer (TFT) changes that — it delivers state-of-the-art accuracy and interpretability.Developed by Google Cloud AI researchers, TFT combines:LSTM encoders/decoders (short-term patterns)Multi-Head Attention (long-term dependencies)Static covariate encoders (..

⚡ Long-Range Time Series Forecasting with Transformers in JAXRNNs and GRUs are great at modeling short-term dependencies,but they struggle when patterns stretch across hundreds of time steps.Transformers, with their self-attention mechanism, can capture these long-range dependencies efficiently.📌 Why Use Transformers for Time Series?Challenge How Transformers HelpLong-term patternsAttention let..

🎯 Probabilistic Time Series Forecasting with DeepAR in JAXMost models we’ve built so far make point forecasts — a single number per future time step.But the real world is messy.Sometimes, we need to know how uncertain our predictions are.That’s where DeepAR comes in.📌 What is DeepAR?DeepAR is a probabilistic forecasting model developed by Amazon.Instead of predicting a single value, it predict..

🌐 Multivariate Time Series Forecasting with Graph Neural Networks in JAXSo far, we’ve looked at time series as independent sequences.But in reality, variables often influence each other:Sensors in a factory lineTraffic speed across city intersectionsStock prices in related sectorsThis is where Graph Neural Networks (GNNs) shine — they let us model relationships between variables explicitly.🔗 W..

🕵️‍♂️ Time Series Anomaly Detection with Variational Autoencoders in JAXUp to now, we’ve focused on forecasting.But forecasting isn’t the only game in town — sometimes you want to know:“Is this time series behaving abnormally?”A Variational Autoencoder (VAE) is perfect for this:It learns a compressed latent representation of normal patternsIf the reconstruction error is high → something unusual..

🧠 ARIMA + Neural Networks in JAX – Hybrid Time Series Forecasting That WorksSometimes deep learning models overkill simple patterns — like a steady trend or seasonality.But classical models like ARIMA handle those beautifully.So… what if we combined them?That’s the idea behind hybrid models:Let ARIMA model the easy, linear stuffLet a neural net learn the leftover noiseHybrid = classical stats +..

🔭 Forecasting with Transformers in JAX – Time Series Gets an Attention UpgradeIn our last post, we trained a simple LSTM forecaster in JAX.It was lightweight and interpretable — but it struggles with long sequences and parallelism.This time, we're bringing out the big guns:🧠 Transformers.Yes, the same architecture behind ChatGPT can be applied to time series forecasting — and it works surprisi..

Time Series Forecasting with LSTM in JAX – A Clean, Minimal ImplementationWelcome back to the JAX Time Series series.We’ve preprocessed our data, normalized it, created sliding windows, and now… it’s time for our first model:a simple LSTM for sequence-to-sequence prediction.If you’re used to PyTorch or Keras, Flax might feel minimal — in a good way.We’ll build a model from scratch and train it o..