The first six parameters are general summary information. The second six parameters are the periodicity information. From the summary, we know that the data is 100% regular because the median and mean differences are 86400 seconds or 1 day.

Machine learning is a powerful way to analyze Time Series. With innovations in the tidyverse modeling infrastructure (tidymodels), we now have a common set of packages to perform machine learning in R. These packages include parsnip, recipes, tune, and...

Today we are demo-ing the h2o package for machine learning on time series data. Every day this week we are demoing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! We'll give you intel on what you need to know about these packages to go from zero to hero. Today you'll see how we can use timetk h2o to get really accurate time series forecasts. The h2o package is a product offered by H2O.ai that contains a number of cutting edge machine learning algorithms, performance metrics, and auxiliary functions to make machine learning both powerful and easy.

Time Series Forecasting is a key area that can lead to Return On Investment (ROI) in a business. Think about this: A 10% improvement in forecast accuracy can save an organization millions of dollars.

This article was published as a part of the Data Science Blogathon. Anomaly detection is a process in Data Science that deals with identifying data points that deviate from a dataset's usual behavior. Anomalous data can indicate critical incidents, such as financial fraud, a software issue, or potential opportunities, like a change in end-user buying patterns. Let us download the dataset from the Singapore Government's website that is easily accessible.- Singapore's government data website is quite easily downloadable.