NeuralProphet: A simple forecasting package

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

A python library for Bayesian time series modeling

ML powered analytics engine for outlier detection and root cause analysis.

A small walk through on how we can decompose a time series into trend, seasonality and residual

The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model. It also contains the implementation and analysis to time series anomaly detection using brutlag algorithm.

Forecasting Monthly Sales of French Champagne - Perrin Freres

Forecasting future traffic to Wikipedia pages using AR MA ARIMA : Removing trend and seasonality with decomposition

Analyze historical market data using Jupyter Notebooks

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…

Using SARIMAX for Time Series Forecasting on Seasonal Data that is influenced by Exogenous variables

Estimating the effect of Hawaiian AirBnB listing characteristics, the time until the booking starts (in days) and the season on the price per night.

Crack spread is the price differential between crude oils and refined products. "Crack" refers to the catalyzing and heating process that results in the breaking down of the carbon bonds, hence the name "cracking". The spreads represent industry refining margins and lend insight into economic activities. The spreads are thought to be seasonal. T…

Using ICESAT-2 to detect topographical changes over land surfaces, in particular permafrost

R code for the paper 'Forecasting seasonal time series data: a Bayesian model averaging approach'

Time Series Analysis

Semesterproject - GEO880 Comuptational Movement analysis - spring semester 2022

Doing predictions of sales for Qatar-based user. Used timeseries, seasonal decomposition and sentiment analysis. Goal was to visualize sales, find connections between hundreds of independent variables and sales and to make the sales of the company and make them better.

Final Project for a Mathematical Biology course

Time series forecasting techniques to predict TESLA's stock price