Top Time Series Forecasting Courses to Watch Out for in 2021

These time series forecasting courses are the best to study to follow a career in the time series field.

To have the ability to look into the future. Wouldn't that be fantastic? We'll undoubtedly get there someday, but time series forecasting can help you get there now. It enables you to "look" ahead of time and achieve success in your business.

Time series forecasting is a machine learning technique that examines data and time sequences to forecast future events. Based on historical time-series data, this methodology delivers near-accurate predictions about future patterns.

Today we have listed the top 10-time series forecasting courses to watch out for in 2021. If you are aspiring to carve a career path out of it, you may want to consider these courses.

Time Series Forecasting

The foundational knowledge needed to create and apply time series forecasting models in a range of business scenarios is provided in the Time Series Forecasting course. You'll study the fundamentals of time series data and forecasting models, as well as a lot more. You'll also learn how to use Alteryx, a data analytics program, to apply what you've learned in this course.

Sequences, Time Series, and Prediction

This specialization will teach you how to use TensorFlow, a prominent open-source machine learning framework. In this fourth course, you'll learn how to use TensorFlow to create time series models. To prepare time series data, you'll first use best practices. You'll also learn how to use RNNs and 1D ConvNets for prediction. Finally, you'll put everything you've learned thus far into practice by creating a sunspot prediction model based on real-world data.

Practical Time Series Analysis

This course will examine data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, agricultural commodity pricing, and so on. You'll also look at a number of mathematical models that may be used to describe the processes that produce this type of data, as well as graphical representations that can help you understand your data. Finally, you'll discover how to construct forecasts that accurately predict what you can expect in the future.

Specialized Models: Time Series and Survival Analysis

This course covers additional Machine Learning techniques that supplement core tasks, such as forecasting and evaluating censored data. You'll discover how to locate and analyze data having a time component, as well as censored data that requires outcome inference. You'll learn a few Time Series Analysis and Survival Analysis approach. This course's hands-on component focuses on recommended practices and testing assumptions generated from statistical learning.

Compare Time Series Predictions of COVID-19 Deaths

You will learn how to preprocess time series data, visualize time series data, and compare the time series predictions of four machine learning models in this 2-hour project-based course. You will use the Python programming language to develop time series analysis models to forecast daily deaths caused by SARS-CoV-19, or COVID-19. The following models will be created and trained: SARIMAX, Prophet, neural networks, and XGBOOST. You'll use the matplotlib library to visualize data, extract features from a time series data set, and partition and normalize the data.

Predict Web Traffic with Trend Forecasting in Google Sheets

By the completion of this project, you will have a solid understanding of the principles of time-series forecasting, which are used to anticipate web traffic flow in order to give useful business intelligence for operations, resource allocation, and opportunity identification. In Google Sheets, you'll be able to forecast web traffic as well. To accomplish this, you'll use the free Google Sheets software to explore trend forecasting and its applications.

Intro to Time Series Analysis in R

You will learn the fundamentals of time series analysis in R in this 2-hour project-based course. You will have created each of the major model types (Autoregressive, Moving Average, ARMA, ARIMA, and decomposition) using a real-world data set to anticipate the future by the end of this project.

Exploratory Time Series Analysis

This project focuses on time series data analysis in Python for beginners. Only after conducting thorough exploratory research and gaining insight into the data set is model construction effective. The following are the goals:

1. Importing needed libraries and time-series data sets.

2. Review the summary of time-series data and obtain basic descriptive statistics.

3. Make inferences from time-series data visualization graphs

4. Examine how time series data behaves.

5. Convert non-stationary data to stationary data using transformation functions.

Covid-19 Cases Forecasting Using Fbprophet

On the basis of historical data, predictive models seek to forecast future value. You will analyze the global transmission of the Covid-19 virus and train a time-series model (fbprophet) to predict corona virus-related infections in the United States in this hands-on project.

Analytics for Decision Making Specialization

This specialization will go through basic predictive modeling approaches for estimating important parameter values, as well as optimization and simulation approaches for formulating judgments based on those parameter values and situational restrictions. The specialization will teach how to use predictive models, linear optimization, and simulation methods to model and solve decision-making problems.