Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

List of papers, code and experiments using deep learning for time series forecasting

Pytorch/Keras implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

list of papers, code, and other resources

Time series prediction using dilated causal convolutional neural nets (temporal CNN)

R package - Dynamic Ensembles for Time Series Forecasting

Web app to predict closing stock prices in real time using Facebook's Prophet time series algorithm with a multi-variate, single-step time series forecasting strategy.

Rainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.

Neural Time Series Analysis

Time series forecasting for common inflators and economic indices using the forecast package in R.

An experiemtal review on deep learning architectures for time series forecasting

Machine Learning Practise

Coding from classical methods applying in time series forecasting

ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It is a class of model that captures a suite of different standard temporal structures in time series data. In statistics and econometrics, and in particular, in time series analysis, an autoregressive integrated moving average model is a generalization of an autoregressive moving average model. Both of these models are fitted to time series data either to better understand the data or to predict future points in the series.

GETS - Time Series Forecasting Framework using Grammatical Evolution.

The GFSM algorithm

Time series analysis written in python in a colab jupyter environment. Predominant libraries used were numpy, pandas and statsmodels.

Python codes for time series forecasting

An LSTM Neural Network for Time Series Forecasting, trained on Wikipedia's Web Traffic dataset from Kaggle.

Time Series Forecasting Models: ETS, ARIMA

Time series forecasting with SARIMA, VAR, Fast Fourier Transform, Exponential Smoothing, Prophet and LSTM Network on US gun violence incidents that result in multiple casualties.

Stock Prediction using Time Series Forecasting

Time series forecasting using Neural Networks

Keras, Tensorflow eager execution layers for exponential smoothing

This part of the work is dedicated to the study of time series, more precisely the ARIMA model and R Packages.

Forecasting customer traffic of a specific form of transportation using SEVEN different forecasting methods based on past traffic data and performing comparative analysis in terms of RMSE.