
Clarify the course structure and expectations for time series analysis and forecasting in R, emphasizing hands-on methods from data preprocessing to ARIMA modeling.
Choose forecasting methods by considering data availability, regular univariate time series versus multivariate, and the mix of qualitative (Delphi method) and quantitative approaches, then communicate the rationale to managers.
Explore base R datasets for time series analysis, including Lake Huron, Notam, Air Passengers, euro stock markets, and sunspots, highlighting stationarity, autocorrelation, seasonality, trend, and multivariate ts objects.
Construct a seven-row data frame with date, time, and measurement columns using date and time input formats. Use lubridate to convert to date and time and combine vectors with cbind.
Learn to create and customize a season plot with the forecast package in R, using the air passengers dataset and options for year labels, label gaps, and plot type.
Learn to handle irregular time series in R by regularizing data through daily aggregation. Use zoo, posixct/posixlt, and strptime to convert dates and build a reliable regular time series.
Analyze time series vectors in R by attaching timestamps, understanding order and lag concepts, and analyzing univariate statistics such as mean, median, and quantiles.
Learn how residuals reveal the randomness left after fitting a time series model, signaling zero mean, constant variance, and uncorrelated behavior, with checks via histograms and acf toward normality.
Explore stationarity in time series, apply transformations and differencing to stabilize data, and use ADF tests in R to guide ARIMA or exponential smoothing modeling.
Study seasonal decomposition for univariate time series with a seasonal component, using additive or multiplicative models, and compare decompose with STL for quick trend and seasonality insights.
Explore key R packages for multivariate time series analysis, including Mtz and VARs, and learn to manage var function differences and package conflicts in vector autoregressive modeling.
Learn how to implement a var model in R, identify the optimal lag order with var select and var, and forecast using a none type, stationary multivariate time series.
Assess multivariate series models by testing residual correlation with a portmanteau test via serial.test on a var object to optimize model fit; consider differencing or logarithms when residuals remain correlated.
Explore how neural networks apply to time series analysis in R, including R packages, univariate and multivariate forecasting with the network function, and demonstrations using smart meter data.
Explore the apt electricity dataset from a smart meter to model high-frequency watt measurements with daily seasonality in time series forecasting in R, using appliances as an external regressor.
Explore neural nets for time series in R via the time series task view, comparing tsd, gmd, and n for packages, with forecast's neural net function as the easiest option.
Explore neural networks in R using the forecast package's enter function for univariate time series with configurable P, size, repeats, and xreg.
Understand the Now – Predict the Future!
Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to
Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!
You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package.
You will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Knowing the best functions for outlier detection, missing value imputation and visualization can safe your day.
After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests.
Then you will see how different models work, how they are set up in R and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied with plenty of exercises.
In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.
Unfortunately learning material on Time Series Analysis Programming in R is quite technical and needs tons of prior knowledge to be understood.
With this course it is the goal to make understanding modeling and forecasting as intuitive and simple as possible for you.
While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course.
It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (course R Basics).
What R you waiting for?