Bindings for Nixtla's neuralforecast Library in R, specifically KANs, to use with {modeltime} package. Nixtla's KAN is called from R via {reticulate}, which is then ported into {parsnip} and bridged to {modeltime}.

how to use nixtla's neuralforecast (SOTA DL) python package via R.

Nixtla time series forecasting plugins for Claude Code. StatsForecast, MLForecast, and NeuralForecast integrations with agent skills.

I conducted an experiment following the ideas of Nie et al. (2003) https://arxiv.org/pdf/2211.14730.pdf However, I changed their setting, using NeuralForecast for predictions on the Exchange dataset, which represents exchange rates of 7 different countries' currencies from 1990 to 2010. I extended the benchmark over other models.

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This project explores the performance of modern deep learning architectures for time series forecasting using the NeuralForecast framework — an open-source library built on top of PyTorch, designed to simplify training and evaluation of state-of-the-art forecasting models.

This project uses NeuralForecast and KAN (Kernel-based Attention Networks) to forecast battery discharge cycles. It processes raw battery data, aggregates capacity per cycle, and trains a model to predict future battery performance. The model is evaluated using metrics like MAE and MSE for prediction accuracy.

A conda-smithy repository for neuralforecast.

simple neuralforecast

use neuralforecast package

UCSD Neural Forecasting SMASH Hackathon

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Stock's price is predicted by TFT of neuralforecast.

repository to experiment with neuralforecast library to predict glucose

Tutorial on time series forecasting with NeuralForecast to be released in LightningAI

Intraday BTC forecasting on 15-min bars with calibrated PIs using NeuralForecast

Some code working on the NeuralForecast library to forecast timeseries with textbook datasets

Minimal reference implementation for running Lightning and NeuralForecast on Intel GPU via native PyTorch XPU.

Repository for demand forecasting projects for various stores and product categories, represented by multiple time series, using the neuralforecast library for transformers in Python

Led a collaborative project on crude oil price forecasting, leveraging advanced machine learning techniques including SARIMA, ARIMAX with seasonality, Prophet model, Neuralforecast Approach, and fine-tuning NBEATS w/Optuna. Sourced data from Yahoo Finance, demonstrating proficiency in diverse forecasting methodologies and financial data analysis.