FelipeMT5

PrevisaoIPCA

Modelos de alta dimensionalidade para previsão do IPCA

HDeconometrics

Set of R functions for high-dimensional econometrics

IPCA_predict

Data scraping and IPCA forecast model

ipca

Inflation forecast with time series

ipca_forecasting

turkey-macro-dashboard

tidytuesday

Official repo for the #tidytuesday project

Article.Datastream.R.InformationDemandAndStockReturnPredictability

This article addresses well established return forecasting challenges via frameworks that focus on the sign of the change in asset index excess returns using a family of GARCH models. It investigates them in the literature's original S&P 500 index to study the predictive power of information demand proxied by Google's internet search vector index and finds evidence suggesting that an efficient trading strategy stemming from this study can be constructed. This article is aimed at academics from undergraduate level up, and thus will explain all mathematical notations to ensure that there is no confusion and so that anyone - no matter their expertise on the subject - can follow.

box_office_success_prediction

Prediction of box office success using Google Trends data

r-pkgs

Building R packages

google_trends

Predicting Google Search Trends

bitcoin-google-trend-strategy

trade bitcoin using simple google trend strategy

Google-trends-stock-data

Correlation between google search trends and stock trading volume

LSTM-Bitcoin-GoogleTrends-Prediction

Recurrent Neural Network (RNN), LSTM (Long Short-Time Memory), Bitcoin, Google Trends, Prediction, Deep Learning

Nowcasting-Python

Python Nowcasting

GoogleTrendsUINowcast

google-trends-daily

Reconstruct daily trends data over extended period

GoogleTrendsAnchorBank

Google Trends, made easy.

stock-market-prediction-via-google-trends

Attempt to predict future stock prices based on Google Trends data.

stock-volatility-google-trends

Deep Learning Stock Volatility with Google Domestic Trends: https://arxiv.org/pdf/1512.04916.pdf

nowcast_lstm

LSTM neural networks for nowcasting economic data.

google_trends_consumption_prediction

This work investigates the forecasting relationship between a Google Trends indicator and real private consumption expenditure in the US. The indicator is constructed by applying Kernel Principal Component Analysis to consumption-related Google Trends search categories. The predictive performance of the indicator is evaluated in relation to two conventional survey-based indicators: the Conference Board Consumer Confidence Index and the University of Michigan Consumer Sentiment Index. The findings suggest that in both in-sample and out-of-sample nowcasting estimations the Google indicator performs better than survey-based predictors. The results also demonstrate that the predictive performance of survey-augmented models is no different than the power of a baseline autoregressive model that includes macroeconomic variables as controls. The results demonstrate an enormous potential of Google Trends data as a tool of unmatched value to forecasters of private consumption.

nowcastDFM

Dynamic factor models (DFM) in R. Easy estimation and new data contributions to changes in prediction.

dfms

Dynamic Factor Models for R

GARCH-RAC

Repository for GARCH tutorial paper in RAC

caret

caret (Classification And Regression Training) R package that contains misc functions for training and plotting classification and regression models

indprod

industrial production forecasting

materiais_estudo_R

Materiais de estudo de R

Reinforcement-learning-trading-agent-using-Google-trends-data

This project is part of my internship at ULiege on Deep RL in stock market trading

doccano

Open source annotation tool for machine learning practitioners.