This software was designed with the purpose of anomaly detection and correction for time series water sensor data. This software was developed using the Logan River Observatory data set.

This repository introduces some basics on time series. It also presents ARIMA models and its variants as well as the Facebook Prophet forecasting model.

The repository gives case studies on short-term traffic flow forecasting strategies within the scope of my master thesis.

Empirical analysis with financial data (MSFT stock returns) in R, with the goal to produce useful forecasts using univariate, multivariate time series models and volatility models.

Tesla Stock Price Prediction using the techniques like Feature Extraction, Feature Importance, ARIMA, SARIMAX, Fourier Transform. Forecasting the future price of the Tesla Stock Price.

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

TimeSeries Analysis-TimeSeries Forecasting-Exponential Smoothing-Arima-Mape Evaluation-Insight Business

forecasting electricity demand for the next 1-2 years using historical monthly consumption data and various forecasting models.

Kaggle challenge asking to predict 3 months of sales for 50 different items at 10 different stores based on the past.

Arima con restricciones

This is my portfolio.

This is a release of data and analysis scripts of the "Associations of inclement weather and poor air quality with non-motorized trail volumes" paper published in Transportation Research Part D.

Database management and data analytics from a car-sharing dataset. The dataset contains information about the customers' demand rate between January 2017 and August 2018.

A multivariate time series forecasting of pollution data using ARIMA, LM & ARIMAX in R

Exploring Time Series in R - This is an exploration of time series analysis that includes moving average, holt-winters smoothing, and ARIMA models.

A demand forecasting model for an E-Commerce retailer, built using KPIs from Google Analytics & implemented in RStudio. Models: time-series, ARIMA, Regression (multivariate & dynamic). Open-source & contributions welcome.

This repository contains a research paper I completed for my Time Series Econometrics class.

Code for the Lancet Digital Health manuscript