This part of the work is dedicated to the study of time series, more precisely the ARIMA model and R Packages.

Predict Total Cases in India using Time Series Forecasting

Assignment codes for Time Series (2020, FGV)

Test the many time-series tools in order to predict future movements in the value of the Canadian dollar versus the Japanese yen.

ARIMA, SARIMA, AUTO-ARIMA

Time Series Forecasting: City and Resort Hotels Bookings forecasting

Random forest regression + ARIMA timeseries modeling to impute metric values and forecast revenue for reporting purposes.

Welcome to the Macroeconomic Forecasting and Causality Analysis repository! Here, we use RATS Econometrics software to analyze and forecast key macroeconomic variables such as Consumer Confidence, Housing Prices, Federal Funds Rate, and Government Job Openings, exploring their interrelationships, mainly through ARIMA and VAR models.

ARIMA Seasonal ARIMA model (Forecasting)

Data Visualization of Human Migration Patterns in South East Asia

Sexto projeto em python dessa vez usando séries temporais para previsão do total de vendas a partir do faturamento mensal total e do faturamento mensal total de cada um dos 5 produtos

The project seeks to perform forecasting of Nigeria's temperature change using machine learning.

Finance projects including ARIMA model for time series prediction, credit scoring modelling and monte carlo simulation on structured product returns

🇩🇪🔋 The project predicts weekly solar energy generation in Germany using seasonal ARIMA model with accuracy of 82% and Time Series Analysis from statsmodels.

Time Series Analysis This repository contains codes for performing time series analysis. The main focus is on data manipulation, visualization, and modeling techniques using libraries such as pandas, numpy, matplotlib, and statsmodels. Key topics include data cleaning, removing duplicates, trend and seasonality decomposition, and forecasting

Project of Data-Science-Lab a.y. 2018/2019

ARIMA model implementation for vehicle`s mileage left until high risk zone and risk of failure prediction using R.

This project is to build an ARIMA model to predict supermarket deals (Coles and Woolworths). It is written in R programming language. The dataset consists of multiple attributes such as date and items name showed on website. The price are updated progressively. The dataset is not having all items, instead only the items of interest.