In this example, neural networks are used to forecast energy consumption of the Dublin City Council Civic Offices between March 2011 – February 2013.

Continue reading »# Category: R

# Keras with R: Predicting car sales

Keras is an API used for running high-level neural networks. The model runs on top of TensorFlow, and was developed by Google. In this particular example, a neural network will be built in Keras to solve a regression problem, i.e. […]

Continue reading »# Kalman Filter: Modelling Time Series Shocks with KFAS in R

We have already seen how time series models such as ARIMA can be used to make time series forecasts. While these models can prove to have high degrees of accuracy, they have one major shortcoming – they do not account […]

Continue reading »# Working with panel data in R: Fixed vs. Random Effects (plm)

Panel data, along with cross-sectional and time series data, are the main data types that we encounter when working with regression analysis.

Continue reading »# Robust Regressions: Dealing with Outliers

It is often the case that a dataset contains significant outliers – or observations that are significantly out of range from the majority of other observations in our dataset. Let us see how we can use robust regressions to deal […]

Continue reading »# Variance-Covariance Matrix: Stock Price Analysis in R (corpcor, covmat)

The purpose of a variance-covariance matrix is to illustrate the variance of a particular variable (diagonals) while covariance illustrates the covariances between the exhaustive combinations of variables.

Continue reading »# Sentiment Analysis with twitteR and tidytext

A sentiment analysis is a useful way of gauging group opinion on a certain topic at a particular point in time. Using social media data, let us see how we can use the twitteR library to stream tweets from Twitter […]

Continue reading »# Cumulative Binomial Probability with R and Shiny

In conducting probability analysis, the two variables that take account of the chance of an event happening are N (number of observations) and λ (lambda – our hit rate/chance of occurrence in a single interval). When we talk about a […]

Continue reading »# plyr and dplyr: Data Manipulation in R

The purpose of the plyr and dplyr libraries in R is to manipulate data with ease. As we’ve seen in a previous post, there are various methods of wrangling and summarising data in R. However, wouldn’t it be great if […]

Continue reading »# ARIMA Models: Stock Price Forecasting with Python and R

ARIMA (Autoregressive Integrated Moving Average) is a major tool used in time series analysis to attempt to forecast future values of a variable based on its present value. For this particular example, I use a stock price dataset of Johnson […]

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