TwentyTwentyOne

💼 Multivariate Bayesian Portfolio Forecasting with JAXIn the last post, we built a Bayesian forecaster for one stock (AAPL).But investors rarely care about a single ticker — portfolios matter.👉 Today, we’ll forecast multiple correlated stocks (AAPL, MSFT, AMZN) using a multivariate Bayesian model.📊 Step 1: Load Portfolio Dataimport yfinance as yfimport pandas as pdtickers = ["AAPL", "MSFT", "..

💹 Bayesian Time Series Forecasting with JAX – Stock Market Case StudyWe’ve built, scaled, and deployed Bayesian forecasting systems with JAX.Now let’s apply these techniques to one of the most challenging domains:👉 Financial stock forecasting.📊 Dataset: Stock Prices (Yahoo Finance)We’ll use daily stock prices for a major ticker (e.g., AAPL).import yfinance as yfimport pandas as pddf = yf.down..

⚡ Scaling Bayesian Time Series Forecasting with JAX on GPU/TPU for Real-Time InferenceSo far, we’ve deployed Bayesian forecasters with FastAPI + Docker and set up MLOps monitoring.But when latency matters — e.g., real-time energy forecasting or high-frequency trading — CPUs aren’t enough.👉 We need GPU/TPU acceleration in production.🧭 Why GPU/TPU for Time Series?Use Case Why Acceleration HelpsR..

🔄 MLOps for Bayesian Time Series Forecasting with JAXWe’ve built powerful Bayesian forecasting models and deployed them with FastAPI + Docker.Now comes the hard part: keeping them accurate and reliable in production.This post covers an MLOps workflow tailored for Bayesian time series forecasting.🧭 The Challenges in ProductionChallenge Why It MattersData DriftInput distribution changes over tim..

🌐 Deploying Bayesian Time Series Forecasting with JAX, FastAPI, and DockerIn the last post, we built a Bayesian LSTM forecaster for energy demand data.Now, let’s turn it into a production-ready API that can:Serve forecasts in real timeReturn uncertainty intervalsRun inside a Docker container🧱 Step 1: Save Model ParametersAfter training, save your model state:import orbax.checkpoint as ocp# Sav..

⚡ Real-World Bayesian Time Series Forecasting with JAX – Energy Demand Case StudySo far, we’ve built:Deterministic forecasters (LSTM, Transformer, TFT)Probabilistic models (Gaussian Likelihood, Quantile Regression)Bayesian approaches (MC Dropout, Variational Inference, SVI)Now let’s apply these ideas to a real-world dataset: electricity demand forecasting.📊 Dataset: UCI Energy Load ForecastingW..

⚡ Scaling Bayesian Time Series Forecasting with SVI in JAXIn the last post, we built a Bayesian Neural Network with Variational Inference.It worked, but training on large time series datasets can be painfully slow.That’s where Stochastic Variational Inference (SVI) comes in — a scalable Bayesian inference method that uses mini-batches and stochastic optimization.🧠 Why SVI for Time Series?Challe..

🔄 Variational Inference for Bayesian Time Series Forecasting in JAXIn the last post, we used MC Dropout as a practical Bayesian trick.Now, let’s go deeper: Variational Inference (VI), a method that learns an approximate posterior over model parameters.Instead of having fixed weights, we let weights be distributions.This gives us a principled Bayesian framework, where we learn q(θ | φ) (variatio..

🧩 Bayesian Time Series Forecasting with JAX – Variational Inference & DropoutWe’ve explored Gaussian likelihood and Quantile Regression for probabilistic forecasting.Now let’s talk Bayesian methods: using priors + posterior distributions to represent uncertainty in our forecasts.Unlike point-estimates, Bayesian models let us ask questions like:What’s the probability demand exceeds a threshold?H..

📐 Quantile Regression for Time Series Forecasting in JAXIn the last post, we introduced probabilistic forecasting with Gaussian likelihood.But Gaussian assumptions don’t always hold — time series data can be asymmetric or heavy-tailed.👉 That’s where Quantile Regression comes in. Instead of predicting a single mean and variance, we directly estimate different quantiles (like the 10th, 50th, and..