NeuralProphet: A simple forecasting package

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

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.

R interface to JDemetra+ v 2.x

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.

Analyze historical market data using Jupyter Notebooks

Forecasting Monthly Sales of French Champagne - Perrin Freres

Extending state-of-the-art Time Series Forecasting with Subsequence Time Series (STS) Clustering to enforce model seasonality adaptation.

Time Series Forecasting Methods — A collection of Python implementations for essential time series forecasting techniques, including Simple, Double, Triple Exponential Smoothing, and Moving Averages.

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 different metrics used to evaluate the forecasts. Goal: The goal of this project was to predict future gold price based on previous gold price. I apply various quantitative methods, (i.e. Times Series Models and Causal Models) to forecast the Price of the gold available in the dataset obtained from Kaggle. Models covered in the Project include: 1.Naive Model 2.ARIMA and Seasonal ARIMA Models 3.Linear Regression Model 4.Model Deployment (Streamlit)

A retail analytics capstone that converts transactions into a calendar intelligence system. It quantifies day-of-week and monthly seasonality, builds a baseline expected revenue model, detects event-like spike days using robust residual z-scores, and explains spikes via transactions, units, AOV, and category mix, with a Streamlit dashboard+exports.

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

Pyriodicity provides an intuitive and efficient Python implementation of periodicity length detection methods in univariate signals.

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

Use Facebook Prophet model to forecast Sales including seasonality patterns

Seasonal adjustment of weekly data

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

Spline-based regression and decomposition of time series with seasonal and trend components.

Time and seasonality features are often ignored as an input in model calibration. Finding the optimal form of seasonality effects should be part of the model-building process. The study investigates the comparative performance of common seasonality treatments, as published in Towards Data Science on Medium.com

Stock market prediction on 5 italian companies using VAR model, OLS regressions and LSTM recurrent neural networks over data retrieved from Refinitiv Eikon

Quantitative research tool analyzing stock performance around US Thanksgiving. 354 stocks, 8,293 observations (2000-2024). Statistical significance testing included.

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

An R package for characterizing temporal data using non-parametric methods for exploratory time series analysis.

Data Analysis of Capital Bikeshare

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

Football matches 2024/2025 EDA tutorial: goals, outcomes, home advantage, seasonality, and team performance.

Finding out various components like trends and seasonality in the time series describing tunnel traffic.

İBB'nin İkitelli'de bulunan güneş enerjisi panellerinin gelecek zamanda üretecekleri toplam enerjinin tahmininin yapılmasına ilişkin oluşturulmuş repository.

This repository is a combination of ML methods for seasnality forecasting