Search Results for "forecasting" - Page 2

Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. ...

Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. ...

SwaNN is a basic framework for neural networks based on particle swarm optimization (using the Python package PySwarms (https://pyswarms.readthedocs.io/en/latest/). The zip file contains the main programs in SwaNN.py and around 30 examples : - classification - regression - time series forecasting I need some help for class building (I am not an expert in Python nor in OOP), if somebody is interested in it... In Google Colab : https://colab.research.google.com/drive/1u6SOydDUThUrhTfaic2NiyDhh1ZGRJsH?usp=sharing What's new: - the jupyter notebook is reorganized and clean

bulbea is an open-source Python library designed for financial analysis and stock market prediction using machine learning and deep learning techniques. The library provides tools for retrieving financial time series data, preprocessing market data, and training predictive models that estimate future price movements. bulbea integrates common machine learning frameworks such as TensorFlow and Keras to build neural network models capable of learning patterns in historical financial data. It...

granite-timeseries-ttm-r2 is part of IBM’s TinyTimeMixers (TTM) series—compact, pre-trained models for multivariate time series forecasting. Unlike massive foundation models, TTM models are designed to be lightweight yet powerful, with only ~805K parameters, enabling high performance even on CPU or single-GPU machines. The r2 version is pre-trained on ~700M samples (r2.1 expands to ~1B), delivering up to 15% better accuracy than the r1 version. TTM supports both zero-shot and fine-tuned forecasting, handling minutely, hourly, daily, and weekly resolutions. ...