Consulting an imaginary real estate investment firm using historical housing sales price data and SARIMAX time series modeling. Flatiron Module 4 Project.

Analyzed historical monthly sales data of a company. Created multiple forecast models for two different products of a particular Wine Estate and recommended the optimum forecasting model to predict monthly sales for the next 12 months along with appropriate lower and upper confidence limits

This is a final project for a Time Series course. My professor told me I could further work on it.

Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

CoronaTracker Covid19 Twitter Data Visualization and Analysis

This repository contains data and code for counterfactual time series analysis of air pollutant concentrations in the United States.

Auto-Forecasting is a web application that takes in an excel file with univariate time series data and provides forecasts. Auto-Forecasting works on SARIMA modeling.

In this project, we leverage time series forecasting techniques to make educated estimates of wine sales throughout the 20th century.

Collecting, analyzing and forecasting sensory data from esp32 sensory devices.

This project is a customizable real estate market forecasting tool with 10 ready-to-use time series SARIMA models of states including New York, California and Texas.

Study and research on the hourly Time Series of electricity price from Italy. My interest would be to obtain both short and long term forecasts. I employ two univariate methods: sARIMA modelling and Prophet

Techniques include EDA, seasonal decomposition, stationarity testing, and implementation of forecasting models like ARIMA, SARIMA, and Holt-Winters (Triple Exponential Smoothing). Models were evaluated using RMSE, with SARIMA and Holt-Winters delivering the best performance for seasonal and trend-based forecasts.