
Explore time series components—trend, seasonality, cyclical, and irregular—and how to decompose monthly dengue data and annual GDP growth to reveal long-term patterns and the presence or absence of seasonality.
Learn to visualize trends in time series data by applying weekly and monthly moving averages, smoothing out outliers and missing values, and comparing actual data to smoothed trends in R.
Explore theta lines for forecasting with time series data, including converting daily mortality data to monthly, imputing missing values by interpolation, and generating 12-month forecasts using theta line models.
Learn to forecast on the fly with the profit package in R, generating 365-day future projections for Apple stock, using make_future_data_frame, predict, and visualization.
Create weekly lags for the E. coli time series by converting data to a ts object, then to zoo, and generate 1–4 week lags for analysis.
See how auto.arima selects parameters for us and forecasts future values using the forecast package in R, with non-seasonal data like U.S. consumption and Apple stock.
Explore bats and tbats models for forecasting time series with multiple seasonalities, decompose data into weekly and monthly components, fit tbats, forecast 12 months, and assess accuracy with backtesting.
Explore selecting best regression model for time series forecasting using H2O AutoML, comparing GBM, random forest, and OLS on beer sales data from 2010–2017, with training, validation, and testing splits.
Learn to forecast humidity over time using recurrent neural networks on temporal weather data, via the Orendain package, training on 2008–2017 data and evaluating with predictions.
Apply recurrent neural networks to time series data, using past four months of chicken prices as predictors. Train with epochs and assess performance on unseen data, noting a 0.58 correlation.
Harness GitHub as your data portfolio and use GitHub Desktop to push local code to repositories, create repos, and collaborate via fetch and pull.
THIS IS YOUR COMPLETE GUIDE TO TIME SERIES DATA ANALYSIS IN R!
This course is your complete guide to time series analysis using R. So, all the main aspects of analyzing temporal data will be covered n depth..
If you take this course, you can do away with taking other courses or buying books on R based data analysis.
In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in in analyzing time series data in R, you can give your company a competitive edge and boost your career to the next level.
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:
Hey, my name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.
I have +5 years of experience in analyzing real life data from different sources using data science related techniques and i have produced many publications for international peer reviewed journals.
Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .
So, unlike other R instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics!
You will go all the way from carrying out data reading & cleaning to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data.
Among other things:
You will be introduced to powerful R-based packages for time series analysis.
You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data.
& you will learn to apply these frameworks to real life data including temporal stocks and financial data.
NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED!
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.
After taking this course, you’ll easily use the common time series packages in R...
You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
We will work with real data and you will have access to all the code and data used in the course.
JOIN MY COURSE NOW!