NeuralProphet: A simple forecasting package

Official repository for the paper "Chunked Autoregressive GAN for Conditional Waveform Synthesis"

The official implementation for ICMI 2020 Best Paper Award "Gesticulator: A framework for semantically-aware speech-driven gesture generation"

A python multi-variate time series prediction library working with sklearn

I perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The Autoregressive Moving Average (ARMA) Model, The Autoregressive Integrated Moving Average (ARIMA) Model, The ARCH Model, The GARCH model, Auto ARIMA, forecasting and exploring a business case.

ARIMA model from scratch using numpy and pandas.

Learning Data Science

This is the official implementation of the paper "Generating Emotive Gaits for Virtual Agents Using Affect-Based Autoregression".

Forecasting sales and economic demand for businesses with a time series approach using NeuralProphet

Delay Embedded Regressive Reduced Order Model

Time Series Analysis Concepts Explained with examples

modeling autonomous guided vehicle using MATLAB Simulink. deterministic and stochastic system identification

R Time series packages not included in CRAN Task View: Time Series Analysis

This is a final project for a Time Series course. My professor told me I could further work on it.

Matlab Machine Learning application for predicting Arsenal F.C. football results during 2013-2014 season using self-programmed multi-class (1-against-rest approach) Naive Bayes and an implementation of AutoRegression.

Developed predictive models like ARIMA and logistic regression to analyze market trends and forecast movements. Employed statistical techniques like moving averages for trend insights and binary outcome predictions in financial analysis.

Time-series forecasting models

Analyse underlying causalities of functional processes

Autoregression on eye-gaze yields intent prediction.

Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step.