NeuralProphet: A simple forecasting package

Jupyter Notebooks Collection for Learning Time Series Models

Learn about Probabilistic Time Series Forecasting, its key techniques, real-world applications, and advantages over traditional forecasting methods.

Graph Neural Networks utilization for Spatiotemporal graphs. These methods will be applied into the problem of forecasting traffic flow on PEMS-Bay, METR-LA and Seattle Loop Datasets

Gold-Price-forecasting In a personal endevaour to learn about time series analysis and forecasting, I decided to reserach and explore various quantitative forecasting methods.This notebook documents contains the methods that can be applied to forecast gold price and model deployment using streamlit, along with a detailed explaination of the diff…

Forecast object trajectory based on history of tracks. Provides a stable and computationally efficient baseline suitable for real-time systems.

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

(finished) S1 Skripsi UGM | forecasting USD | RNN and UKF

AI system for energy demand forecasting and grid optimization. Uses time series analysis and weather data to predict energy consumption, optimize renewable energy integration, and prevent grid failures.

Machine Learning Competition by BUYAK

Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Businesses utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time

Forecasting using simple linear regression method, case study forecasting the number of visitors to a mall. Demo: https://kevinmalikfajar.alwaysdata.net/forecasting-regresi-linear-sederhana/

This repository contains an ML project that was approached with a business mindset from the beginning to the end. It addresses the problem of forecasting.

The UK Tech sector holds a market capitalisation of £860 billion, making it the third largest globally. Accurate forecasting of stock returns for FTSE TechMark listed firms is essential for investors to make precise investment decisions. This study comparatively evaluates ARIMA and GJR-GARCH(1,1) models across 10 top UK technical firms listed on th

Analysis and forecasting monthly Atmospheric Carbon Dioxide Emissions from March, 1951 to December, 2021 Analysis and Computing done in R studio software

Trying the Temporal Fusion Transformer model for forecasting Renewable energy.

Built a model to predict the Sales of a store

Forecasting problem for Sales of a Plastic manufacturer

A comparison of time-series forecasting models on a weekday-only data using StatsForecast library.