# Time Series Cheat Sheet in R

Sun, Jan 26, 2020 5-minute read

Getting started using the forecast package for time series data in R, as quickly as possible and no explanations.

# Data Prep

Coerce your data to ts format:

library(tidyverse)
library(forecast)

myts <- ts(df, start = c(1981,1), frequency = 12)


# Exploring and Plotting ts Data

• autoplot(): Useful function to plot data and forecasts

## Seasonality

• ggseasonplot(): Create a seasonal plot
• ggsubseriesplot(): Create mini plots for each season and show seasonal means

## Lags and ACF

• gglagplot(): Plot the time series against lags of itself
• ggAcf(): Plot the autocorrelation function (ACF)

## White Noise and the Ljung-Box Test

White Noise is another name for a time series of iid data. Purely random. Ideally your model residuals should look like white noise.

You can use the Ljung-Box test to check if a time series is white noise, here’s an example with 24 lags:

Box.test(data, lag = 24, type="Lj")


p-value > 0.05 suggests data are not significantly different than white noise

# Model Selection

The forecast package includes a few common models out of the box. Fit the model and create a forecast object, and then use the forecast() function on the object and a number of h periods to predict.

Example of the workflow:

train <- window(data, start = 1980)
fit <- naive(train)
checkresiduals(fit)
pred <- forecast(fit, h=4)
accuracy(pred, data)


## Naive Models

Useful to benchmark against naive and seasonal naive models.

• naive()
• snaive()

## Residuals

Residuals are the difference between the model’s fitted values and the actual data. Residuals should look like white noise and be:

• Uncorrelated
• Have mean zero

And ideally have:

• Constant variance
• A normal distribution

checkresiduals(): helper function to plot the residuals, plot the ACF and histogram, and do a Ljung-Box test on the residuals.

## Evaluating Model Accuracy

Train/Test split with window function:

window(data, start, end): to slice the ts data

Use accuracy() on the model and test set

accuracy(model, testset): Provides accuracy measures like MAE, MSE, MAPE, RMSE etc

Backtesting with one step ahead forecasts, aka “Time series cross validation” can be done with a helper function tsCV().

tsCV(): returns forecast errors given a forecastfunction that returns a forecast object and number of steps ahead h. At h = 1 the forecast errors will just be the model residuals.

Here’s an example using the naive() model, forecasting one period ahead:

tsCV(data, forecastfunction = naive, h = 1)


# Many Models

## Exponential Models

• ses(): Simple Exponential Smoothing, implement a smoothing parameter alpha on previous data
• holt(): Holt’s linear trend, SES + trend parameter. Use damped=TRUE for damped trending
• hw(): Holt-Winters method, incorporates linear trend and seasonality. Set seasonal=“additive” for additive version or “multiplicative” for multiplicative version

### ETS Models

The forecast package includes a function ets() for your exponential smoothing models. ets() estimates parameters using the likelihood of the data arising from the model, and selects the best model using corrected AIC (AICc) * Error = {A, M} * Trend = {N, A, Ad} * Seasonal = {N, A, M}

## Transformations

May need to transform the data if it is non-stationary to improve your model prediction. To deal with non-constant variance, you can use a Box-Cox transformation.

BoxCox(): Box-Cox uses a lambda parameter between -1 and 1 to stabilize the variance. A lambda of 0 performs a natural log, 1/3 does a cube root, etc while 1 does nothing and -1 performs an inverse transformation.

Differencing is another transformation that uses differences between observations to model changes rather than the observations themselves.

## ARIMA

Parameters: (p,d,q)(P,D,Q)m

|Parameter|Description | |——————————-| |p| # of autoregression lags | |d| # of lag-1 differences | |q| # of Moving Average lags | |P| # of seasonal AR lags | |D| # of seasonal differences | |Q| # of seasonal MA lags | |m| # of observations per year | |——————————-|

Arima(): Implementation of the ARIMA function, set include.constant = TRUE to include drift aka the constant

auto.arima(): Automatic implentation of the ARIMA function in forecast. Estimates parameters using maximum likelihood and does a stepwise search between a subset of all possible models. Can take a lambda argument to fit the model to transformed data and the forecasts will be back-transformed onto the original scale. Turn stepwise = FALSE to consider more models at the expense of more time.

## Dynamic Regression

Regression model with non-seasonal ARIMA errors, i.e. we allow e_t to be an ARIMA process rather than white noise.

Usage example:

fit <- auto.arima(data, xreg = xreg_data)
pred <- forecast(fit, xreg = newxreg_data)


## Dynamic Harmonic Regression

Dynamic Regression with K fourier terms to model seasonality. With higher K the model becomes more flexible.

Pro: Allows for any length seasonality, but assumes seasonal pattern is unchanging. Arima() and auto.arima() may run out of memory at large seasonal periods (i.e. >200).

# Example with K = 1 and predict 4 periods in the future
fit <- auto.arima(data, xreg = fourier(data, K = 1),
seasonal = FALSE, lambda = 0)
pred <- forecast(fit, xreg = fourier(data, K = 1, h = 4))


## TBATS

Automated model that combines exponential smoothing, Box-Cox transformations, and Fourier terms. Pro: Automated, allows for complex seasonality that changes over time. Cons: Slow.

• T: Trigonemtric terms for seasonality
• B: Box-Cox transformations for heterogeneity
• A: ARMA errors for short term dynamics
• T: Trend (possibly damped)
• S: Seasonal (including multiple and non-integer periods)