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This project aims at studying temporal behaviour of smartphone app users, with special focus at changes in usage. The analysis will rely on time series techniques to detect change points and forecast shifts in usage. In particular, we will leverage univariate timeseries approaches.
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.
The time-series tools (Time Series Forecasting and Linear Regression Modeling ) in order to predict future movements in the value of the Japanese yen versus the U.S. dollar.
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.