5 Best Time Series Analysis Courses For Beginners in 2022

Time series analysis is a statistical technique used to discover patterns in data over time. Learn Time series analysis as a beginner with these Top 5 courses online!

5 Best Time Series Analysis Courses For Beginners in 2022

To evaluate the target variable under the predicted or predicted name, use the temporary variable as a reference point. Time series analysis (TSA) is used in various fields of weather forecasting, such as weather forecasting, finance, signal processing, engineering-control systems, and communication systems.

Time series analysis is a statistical technique for processing time-series data or trend analysis. Analyzing time-series data can provide information such as trends, seasonal patterns, and future event forecasts, which can help you make money.

Keeping this in mind, here at Coursesity, we have curated some of the Best Online Time Series Analysis Courses with certification. Hope that you will find the best course for you to learn how to gather information about the occurrence and frequency of seasonal fluctuations such as weather forecasting, finance, and communication systems.

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Top Time Series Analysis Tutorials List

  1. Time Series Analysis in Python 2021

  2. Practical Time Series Analysis

  3. Time Series Analysis and Forecasting using Python

  4. Applied Time Series Analysis and Forecasting with R

  5. Time Series Analysis, Forecasting, and Machine Learning

1. Time Series Analysis in Python 2021

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, and Forecasting.

In this course, you will:

  • Encounter special types of time series like White Noise and Random Walks.
  • Learn about "autocorrelation" and how to account for it.
  • Learn about accounting for "unexpected shocks" via moving averages.
  • Discuss model selection in time series and the role residuals play in it.
  • Comprehend stationarity and how to test for its existence.
  • Acknowledge the notion of integration and understand when, why, and how to properly use it.
  • Realize the importance of volatility and how we can measure it.
  • Forecast the future based on patterns observed in the past.

With this Time Series Analysis course, you will learn how to differentiate between time series data and cross-sectional data. You will understand the fundamental assumptions of time series data and how to take advantage of them.

Next, you will learn how to transform a data set into a time series and how you can start coding in Python to learn how to use it for statistical analysis. The course will also carry out time-series analysis in Python and interpret the results, based on the data in question.

Then, you will learn how you can examine the crucial differences between related series like prices and returns. Learn how to comprehend the need to normalize data when comparing different time series.

You can take Time Series Analysis in Python 2021 certificate course on Udemy.

Course rating: 4.5 out of 5.0 ( 1,633 Ratings total)
Duration: 7 h 30 m
Certificate: Certificate on completion

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2. Practical Time Series Analysis

By the State University of New York.

The course includes:

  • Basic Statistics
  • Visualizing Time Series, and Beginning to Model Time Series
  • Stationarity, MA(q), and AR(p) processes
  • AR(p) processes, Yule-Walker equations, PACF
  • Akaike Information Criterion (AIC), Mixed Models, Integrated Models
  • Seasonality, SARIMA, Forecasting

In this Time Series Analysis course, you will look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more.

Next, you will look at several mathematical models that might be used to describe the processes which generate these types of data. You will also look at graphical representations that provide insights into our data.

Finally, the course will teach you how to make forecasts that say intelligent things about what you can expect in the future.

You can take the Practical Time Series Analysis certification course on Coursera.

Course rating: 4.6 out of 5.0 ( 1,397 Ratings total)
Duration: 26 h
Certificate: Certificate on purchase

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3. Time Series Analysis and Forecasting using Python

Learn about time series analysis & forecasting models in Python |Time Data Visualization|AR|MA|ARIMA|Regression| ANN

In this course, you will:

  • Get a solid understanding of Time Series Analysis and Forecasting.
  • Understand the business scenarios where Time Series Analysis is applicable.
  • Build 5 different Time Series Forecasting Models in Python.
  • Learn about Autoregression and Moving average Models.
  • Learn about ARIMA and SARIMA models for forecasting.
  • Use Pandas DataFrames to manipulate Time Series data and make statistical computations.

This Time Series Analysis course will teach you how to implement time series forecasting and time series analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA, etc.

Next, you will learn how to implement multivariate time series forecasting models based on Linear regression and Neural Networks.

Finally, the course will teach you how to confidently practice, discuss and understand different time series forecasting, time series analysis models, and Python time series techniques used by organizations.

You can take Time Series Analysis and Forecasting using Python certificate course on Udemy.

Course rating: 4.3 out of 5.0 ( 971 Ratings total)
Duration: 13 h 30 m
Certificate: Certificate on completion

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4. Applied Time Series Analysis and Forecasting with R

Learn Time Series Analysis step by step and work on real-world projects and data.

The course includes:

  • Using R for Time Series Analysis
  • Modeling Unemployment Rates
  • Forecasting Inflation Rates
  • Predicting Sales Using Neural Networks

In this course, Applied Time Series Analysis and Forecasting with R, you’ll learn how to apply modern-day time series models to real-world data.

First, you will discover how to design time series models containing trend or seasonality. Next, you will delve further into models, such as ARIMA, exponential smoothing, and neural networks.

Finally, you will learn how to visualize time series interactively with dygraphs. When you are finished with this course, you will have the necessary knowledge to apply standard time series models on a univariate time series.

You can take Applied Time Series Analysis and Forecast with R certification course on Pluralsight.

Course rating: 4.9 out of 5.0 ( 12 Ratings total)
Duration: 2 h
Certificate: Certificate on completion

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5. Time Series Analysis, Forecasting, and Machine Learning

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting.

In this course, you will:

  • Time Series Basics
  • Exponential Smoothing and ETS Methods
  • ARIMA
  • Vector Autoregression (VAR, VMA, VARMA)
  • Machine Learning Methods
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)

Here, you will cover modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

Plus, you will encounter techniques like Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests) & Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks).

You can take Time Series Analysis, Forecasting, and Machine Learning certificate course on Udemy.

Course rating: 4.8 out of 5.0 ( 419 Ratings total)
Duration: 22 h 30 m
Certificate: Certificate on completion

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