Honours Dissertation Resources

Analysis and forecasting of a retail brand's sales using ARIMA and Random Forests.

This repository is dedicated to the work done for the analysis of FDI in India using time-series statistical model like ARIMA.

Forecast (Australian monthly gas production) for the next 12 periods. Developed an ARIMA and AUTO ARIMA Model to forecast for next 12 periods.

This respiratory provides pre-processed Chicago Crime data along with the code that predicts the future Crime Rates using an ARIMA model.

Forecasted with Time Series Analysis and Regression for a potential outlook on the volatility of the yen. For the regression analysis, the preparation of the data required lagging returns, and after was used for a Linear Regression model. As for the Time Series Analysis, a Hodrick-Prescott filter was used which was followed by ARMA and ARIMA for…

By Using Statistical Model ( ARIMA ) to Predict the Quantity Demand in future

Time Series Forecasting

Predicting the value of an obfuscated metric relevant for making trading decisions.

Modelos de Previsão de COVID-19 de Machine Learning no Brasil por Regressão Logística de Prophet e Modelo ARIMA com Python e Jupyter - [Pandas, Numpy, Datetime, Plotly.Express, Plotly.Graph_Objects, Matplotlib, Seasonal_Decompose, Prophet & ARIMA]

This document summarizes how to use ARIMA model, why do we use ARIMA?, the assumptions of ARIMA model with hypothesis test, and the algorithm of time series ARIMA model implementing in daily bitcoin price with computed volatility for predicting values of its cryptocurrency in the future.

This project involves time series forecasting through EDA and uses ARIMA and SARIMA models. The goal is to analyze and understand data trends, then make accurate forecasts.

This repository contains a Jupyter notebook that demonstrates time series analysis and forecasting using ARIMA, auto-ARIMA, and Prophet. Time series analysis is a powerful tool for understanding and predicting future trends, and these techniques are widely used in a variety of fields such as finance, economics, and marketing. The notebook is based

A project that involves using Deep Learning to forecast stock prices.

This project focuses on Time Series Analysis techniques, uncovering patterns and leveraging forecasting models to predict future sales trends.

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

This project focuses on Linear Regression in R and potential enhancements

Sequence Models

Collected stock price data of Apple Inc. from Yahoo Finance & Business/Financial news data using NYTimes Api and Intrinio Api. Analyzed trend in stock price data using ARIMA Time Series model. Analyzed the news as positive/negative sentiments to prove a co-relation between stock price change and news sentiments helping us to predict the change i…