NeuralProphet: A simple forecasting package

Jupyter Notebooks Collection for Learning Time Series Models

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…

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

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

This repository is dedicated to the group project for the Time Series and Forecasting exam.

Forecasting problem for Sales of a Plastic manufacturer

Trying the Temporal Fusion Transformer model for forecasting Renewable energy.

A general purpose multivariate forecasting algorithm built using pytorch

Built a model to predict the Sales of a store

Machine Learning Competition by BUYAK

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.

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

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

Predecitve model for Stock Return forecast (future prediction) for FTS100 Tech-Mark Series (top technical firms) in UK listed on London Stock Exchange

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/

Conducting Market Forecasting using Excel with different modeling algorithms.

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