NeuralProphet: A simple forecasting package
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Updated
May 21, 2024 - Python
NeuralProphet: A simple forecasting package
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.
A python multi-variate time series prediction library working with sklearn
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"
Learning Data Science
ARIMA model from scratch using numpy and pandas.
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.
Time Series Analysis Concepts Explained with examples
This is the official implementation of the paper "Generating Emotive Gaits for Virtual Agents Using Affect-Based Autoregression".
🏅 2019 한국통계학회 춘계학술논문대회 프로젝트
Implement gradient descent in linear regression problems, construct and evaluate simple linear models, and use feature engineering to create more complex supervised machine learning models.
Forecast for 3 methods of US emissions of CO2 to the atmosphere
R Shiny application for measuring the effect of foods on gastrointestinal symptoms. Public on shinyapps.io
Prediction of the temperature in Berlin Tempelhof for the next couple of days. The model predicted 19.3° C for the first unknown day with the actual temperature being 19.8 °C.
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
Prediction of future global land temperature based on accuracy of different models and evaluating which model performs better
We predict GDP growth in R, comparing autoregressive models.
Autoregression on eye-gaze yields intent prediction.
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