COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data was also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Agriculture plays a very important role in a developing country like India.

Welcome to the Python for Time Series - Data Analysis & Forecasting course. This course is built for students who wants to learn python applications for time series data sets. This course covers the usage of Python libraries on time series data. There will be both short lectures of statistics and Python fundamentals at the starting of the course in order to remembering the basics. Then the libraries of Python which is used for time series data will be covered.

Time Series Analysis can feel familiar and completely foreign at the same time, even to experienced data scientists. It plays by a similar, yet different, set of rules compared to typical classification or regression modeling. Still, Time Series Analysis has applications across industries. Familiar applications such as demand prediction to properly stock the shelves of a store or generate enough electricity to power a city, and less familiar applications such as signal classification to detect level shifts or changes in the underlying behavior of a time series to detect market shifts early. Delving into the world of Time Series Analysis is significantly easier in a low-code environment, enabling the learning and application of new techniques without the requirement of learning new coding libraries at the same time.

The objective of this training program is to help trainees to master all the skills that are required to work with Minitab. The training program will help the trainee to perform all the statistical analysis with Minitab. It is also intended to make the trainees cover all the topics that fall under the domain of Minitab. Topics like Minitab GUI and Descriptive Statistics, Statistical Analysis using Minitab, Correlation Techniques in Minitab and Predictive Modeling using Excel will be covered in this training module and Project on Data Analytics using Minitab and Project on Minitab – Regression Modeling will be covered in the project module. The goal of this course is to help an individual to achieve knowledge of working with Minitab to perform time series analysis and forecasting of data in all sorts of statistics based problems.

A time series is a sequence of data points, typically consisting of successive measurements made over a time interval.

There are many R packages for working with Time Series data. Here's how timetk compares to the "tidy" time series R packages for data visualization, wrangling, and feature engineeering (those that leverage data frames or tibbles). Timetk is an amazing package that is part of the modeltime ecosystem for time series analysis and forecasting. Your probably thinking how am I ever going to learn time series forecasting. Here's the solution that will save you years of struggling.

In this course the student will learn the theory of time series analysis and forecasting. Time series analysis is part of artificial intelligence (AI) and is used by many companies to make predictions on sales, temperature, energy consumption, stock prices, etcetera. Time series analysis involves looking at the time series and making judgements based on the look of the time series. The time series may need to be changed in an attempt to analyse it, and these changes could involve resampling or transforming in some fashion. Time series forecasting involves making predictions on the time series.

If anyone has questions about which course may work best for them, please feel free to contact or message me. I will teach you the real-world skills necessary to stand out from the crowd. Hardly it can be 8-10 hours.. Professionally, I am a Data Scientist having experience of 7 years in finance, E-commerce, retail and transport. From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you!

Time series is a sequence(series) of data that arranged by certain(uniform) interval of time. Among them, discrete time series is a case that a set T of the time t that occurred is discrete, if time t occurs continuously then this is a continuous time series. In general, Time Series Sequence are self-correlated. Namely, data from the past affects beyond the present and into the future. It means covariance of one and others are not equal to zero.