Machine learning and data analysis package implemented in JavaScript and its online demo.

Pytorch-based RAND implementation

If you can measure it, consider it predicted

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

The VAR model is used to forecast the appliances energy on the previous usage history. The data were first tested using adfuller test, granger casuality test. The lag value of 7 was determined for VAR model after running it iteratively for values upto 48.

This project aims to predict gold prices using various time series forecasting techniques. The dataset consists of monthly gold futures data over the last ten years. The primary methods used in this analysis include ARIMA, Error Trend Seasonal (ETS) models, and Exponential Smoothing techniques. The forecast horizon is set for the next two years.

A comprehensive machine learning project using Facebook's Prophet to forecast future sales. The model utilized historical data and effectively accounted for various factors, including seasonality effects, demand fluctuations, holiday impacts, promotional activities, and competitive influences.

This research investigates flight delay trends, examining departure time, airline, and airport factors. Regression machine learning meth- ods are utilized to predict delay contributions from various sources. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decisi

The primary objective of this project is to develop a cutting-edge forecasting model utilizing advanced machine-learning algorithms and sophisticated time-series analysis techniques. The model aims to deliver precise predictions of future sales across diverse retail outlets.

Forecasting the Adoption Process of Technology Using ML Methods

Collection of two projects applying Deep Neural Networks on real-world problems. The first one with CNNs for image recognition and the other with RNNs and 1D-CNNs for timeseries forecasting.

Time series forecasting with PyTorch

Predicting future sales for a company is one of the most important aspects of strategic planning.

This repository contains code and sample data related to the title "InSAR time series clustering and Landslide display prediction with multiple variables and time series: A case study of the Badui area in Eastern Tibet", mainly focusing on clustering and prediction of time series.

A multiverse of Prophet models for timeseries

TSForecasting - Automated Time Series Forecasting Framework

Energy Forecast Benchmark Toolkit is a Python project that aims to provide common tools to benchmark forecast models.

Forecasting gold prices with machine learning, employing Linear Regression and Naive models. Analyzing historical data to predict future prices, aiding decision-making in financial markets.

Analisis Runtun Waktu- Universitas Sanata Dharma

This repository contains projects that forecast the values of data using historical information.