Scalable and user friendly neural 🧠 forecasting algorithms.

Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.

Time-Series models for multivariate and multistep forecasting, regression, and classification

📈 toolset for time series forecasting

This repository is the implementation of the paper: ViT2 - Pre-training Vision Transformers for Visual Times Series Forecasting. ViT2 is a framework designed to address generalization & transfer learning limitations of Time-Series-based forecasting models by encoding the time-series to images using GAF and a modified ViT architecture.

Julia (Flux) implementation of NBeats

Forecast Carbon Emissions with Time-Series data. This repository contains 2x Jupyter Notebooks that predict Carbon Emissions in the United States using Neural Basis Expansion Analysis for Time series (NBeats). The second notebook has an extra pre-processing step of data been scaled and inverse-transformed before final results.

A bitcoin price forecaster utilizing an ensemble of autoregressive, N-BEATS, LSTM and layer normalized models, trained on various loss functions.

Time Series prediction using Nbeats

Exploration into why trying to predict crypto prices is a bad idea.

An autoregressive forecasting implementation of a LSTM network, NBEATS architecture, ARIMA and SARIMAX regressions, and Autoformer architecture on rupee dollar exchange rates using pytorch, pytorch lightning, pytorch-forecasting, and GluonTS

predict price of bitcoin using time series

Dynamic and user-friendly web app for stock prediction

Custom implemented NBEATS algorithm tailored for Bitcoin price prediction, alongside explored a range of models, such as ensembles, naive methods, CONV1D, LSTMs, and dense models, applied to both univariate and multivariate datasets for predicting Bitcoin prices.

My implementations of N-BEATS and TCN models

Knowledge of various Time Series Forecasting topics: Long Short-Term Memory (LSTM), Exponential Smoothing, Autoregressive integrated moving average (ARIMA), TBATS, Multivariate Time Series Forecasting, XGboost, N_BEATS, and Prophet.