I am working at AnalyticaHouse as a Senior SEO Specialist and I have been working in the field of SEO for more than 3 years. During the 3 years I have worked, I interpreted the organic traffic data of the customers and prepared competitor analyzes. In all of these works, I tried to act data-driven. At AnalyticaHouse, I started working with many brands in the e-commerce sector. In addition to SEO studies, we worked closely with the Analytics team to develop new report formats based on customer needs. The fact that we act as a team focused on data has not only limited the interpretation of the existing data but also increased my motivation to shape the data. I decided to extend this motivation not only to business experience but also to the predictions of the academy.
In his presentation “Tidy Forecasting in R”, Rob J Hyndman described the new features of the Fable Package and solved 3 examples. From 2005 to 2018 Rob J Hyndman was Editor-in-Chief of the International Journal of Forecasting and a Director of the International Institute of Forecasters. The R package Fable provides a collection of commonly used univariate and multivariate time series forecasting models including exponential smoothing via state-space models and automatic ARIMA modeling. The Fable Package, which replaces the Forecast Package, provides: Integrating with tidyverse packages, designed for forecasting many related time series, consistency of interface using formula, flexible transformations, etc.
ARIMA Forecasting real life Example in R
In this video (part of the time series forecasting) a real-life example has been taken of rainfall in India and predicted the future year’s rains by producing the Arima model and then using the forecast package. The next few years of rainfall values were predicted.
Introduction to Forecasting with ARIMA in R
ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. This type of model is a basic forecasting technique that can be used as a foundation for more complex models. In this tutorial, there is an example and a checklist for basic ARIMA Modeling.
The R package forecast provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. I should try this package!