Practical Time Series Analysis
Learn time series analysis techniques using R. Master forecasting, ARIMA models, and seasonal adjustments for data-driven decision making.
Learn time series analysis techniques using R. Master forecasting, ARIMA models, and seasonal adjustments for data-driven decision making.
This intermediate-level course provides a comprehensive introduction to practical time series analysis. Designed for professionals with some technical background, it offers more than a cookbook approach to analyzing sequential data like stock prices, rainfall, or sunspot activity. The course covers mathematical models for time series data, graphical representations for insights, and forecasting techniques. Using R programming language, learners will gain hands-on experience in analyzing real-world datasets. Topics include basic statistics review, visualization of time series, stationarity concepts, moving average and autoregressive processes, ARIMA models, seasonal adjustments, and forecasting methods. The course emphasizes understanding the underlying mathematics while focusing on practical applications and immediate productivity in time series analysis.
4.6
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English
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What you'll learn
Understand and apply time series analysis techniques to real-world data
Master R programming for time series visualization and modeling
Learn to identify and model stationary and non-stationary processes
Develop proficiency in ARMA, ARIMA, and SARIMA model fitting and selection
Gain skills in forecasting using various methods including exponential smoothing
Understand and apply autocorrelation and partial autocorrelation functions
Skills you'll gain
This course includes:
2 Hours PreRecorded video
19 assignments
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FullTime access
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There are 6 modules in this course
This course offers a comprehensive exploration of practical time series analysis techniques. Starting with a review of basic statistics, it progresses through visualizing time series data, understanding stationarity concepts, and exploring various time series models. Participants will learn about autoregressive (AR), moving average (MA), and mixed ARMA processes, as well as integrated models like ARIMA. The course covers advanced topics such as seasonal adjustments with SARIMA models and different forecasting methods including exponential smoothing. Throughout the course, learners will use R programming to analyze real-world datasets, gaining practical skills in model fitting, selection using Akaike Information Criterion, and forecasting. The curriculum balances theoretical understanding with hands-on application, preparing learners to tackle real-world time series analysis challenges.
WEEK 1: Basic Statistics
Module 1 · 3 Hours to complete
Week 2: Visualizing Time Series, and Beginning to Model Time Series
Module 2 · 3 Hours to complete
Week 3: Stationarity, MA(q) and AR(p) processes
Module 3 · 5 Hours to complete
Week 4: AR(p) processes, Yule-Walker equations, PACF
Module 4 · 4 Hours to complete
Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models
Module 5 · 4 Hours to complete
Week 6: Seasonality, SARIMA, Forecasting
Module 6 · 4 Hours to complete
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Instructors
Lecturer at The State University of New York Specializing in Time Series Analysis
Tural Sadigov is a Lecturer at The State University of New York, specializing in time series analysis. He teaches the course Practical Time Series Analysis, which focuses on analyzing sequential data sets, such as stock prices and annual rainfall. The course covers essential concepts in time series forecasting, including statistical models and graphical representations to derive insights from data. It is designed for learners with some technical background who wish to deepen their understanding of data analysis techniques relevant to various fields.
Associate Professor at The State University of New York Specializing in Time Series Analysis
William Thistleton is an Associate Professor at The State University of New York, where he teaches the course Practical Time Series Analysis. His expertise lies in analyzing time-dependent data, which is crucial for various applications across fields such as finance, economics, and environmental science. In this course, students learn essential techniques for modeling and forecasting time series data, enabling them to derive meaningful insights from historical trends and patterns. William's teaching approach emphasizes practical application, equipping learners with the skills necessary to tackle real-world data challenges.
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4.6 course rating
1,676 ratings
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