A python library for Bayesian time series modeling

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

Forecasting Monthly Sales of French Champagne - Perrin Freres

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

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

Binary cat image/series classifier based on a deep neural network implemented from scratch using He Random initialization, Adam optimization, RELU and sigmoid activation with regularization

Various models for forecasting Poisson processes with cyclic underlying variability

Dynamic Novel Tree Based Clustering Framework(Personalization). End to End solution.

This repository contains methylation AFLP data as employed in Rodríguez-Casariego et al., Frontiers in Marine Sciences, 2020: "Genome-wide DNA methylation analysis reveals a conserved epigenetic response to seasonal environmental variation in the staghorn coral Acropora cervicornis"

Predicting disease spread, a DrivenData competition. I'am currently participating in this competition. I used it as submission for the second capstone project in the course 'Professional Certificate in Data Science' provided by Harvard University (HarvardX) on EDX.

Buys Ballot plot as a graphical method for detecting seasonality

Seasonality and text analysis of Boston Airbnb data

Forecasting with Seasonality using Exponential Smoothing

Final Project for a Mathematical Biology course

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

Time Series Analysis

This project deals with Time Series Problem to predict the number of passengers in a train over the next 7 months

Seasonal decomposition of time-series (classical, STL) for gretl

This project is to analyze and compare Airbnb listing price in Boston and Seattle, to find any trends, and to identify what factors affect the Airbnb listing price.

MATH-342 Time Series course taken at EPFL during Spring 17-18.

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

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. The below code investigates a few crack spreads visually to establish such relationships.

Statistical analysis of products, customers and sellers from Olist.com