Time Series Analysis with R
Predicting the future is a tough problem. Some things, like lottery numbers, are inherently unpredictable. Others, like air temperatures and rainfall, are reasonably predictable. Time series analysis makes it possible to assess whether or not predictions are possible and, if they are, build a model which can generate informed predictions for the future with realistic estimates of uncertainty.
The best qualification of a prophet is to have a good memory — George Savile
This training course will provide you with an understanding of the theory behind time series models and the ability to build such models in R. By the end of the course you’ll be able to select the appropriate model for your data, train a model and start making predictions.
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Time Series Introduction
- What is a Time Series?
Time Series Objects
- Base Representation
- Creating a
- Creating a
- Other Representations
- Correlation and causality
- Partial Autocorrelation
- Stationarity Revisited
- Autoregressive (AR) models
- Moving Average (MA) models
- Correlations and ARIMA Models
- Simulating ARIMA
- Fitting ARIMA
- Forecasting with ARIMA Models
- Simple Exponential Smoothing
- The Holt Method
- The Holt-Winters Method
- Fitting Exponential Smoothing
- Forecasting with Exponential Smoothing Models
- Moving averages and smoothing
- Additive and Multiplicative Decomposition
- STL Decomposition
- Decomposition and Forecasting