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# What is Time Series Analysis? Part I

This article shows you how to use the R statistical software to carry out some simple analyses that are common in analyzing time series data. If the reader has some basic knowledge of time series analysis it will serve them well since the principal focus of the article is not to explain time series analysis, but rather to explain how to carry out these analyses using R.

Sometimes the time series data set that you have may have been collected at regular intervals that were less than one year, for example, monthly or quarterly. In this case, you can specify the number of times that data was collected per year by using the ‘frequency’ parameter in the ts() function. For monthly time series data, you set frequency=12, while for quarterly time series data, you set frequency=4.

You can also specify the first year that the data was collected, and the first interval in that year by using the ‘start’ parameter in the ts() function. For example, if the first data point corresponds to the second quarter of 1986, you would set start=c(1986,2).

An example is a data set of the monthly live births (adjusted) in thousands for the United States, 1946-1979. The set ‘birth’ is part of the astsa package.

```> require(astsa) > birth ## monthly live births (adjusted) in thousands for the United States, 1946-1979.```

Once you have read the time series data into R, the next step is to store the data in a time series object in R, so that you can use R’s many functions for analyzing time series data. To store the data in a time series object, we use the ts() function in R. For example, to store the data in the variable ‘birth’ as a time series object in R, we type:

```< birthtimeseries < birthtimeseries      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1946 295 286 300 278 272 268 308 321 313 308 291 296 1947 294 273 300 271 282 285 318 323 313 311 291 293 1948 297 273 294 259 276 294 316 325 315 312 292 301 1949 304 282 313 296 313 307 328 334 329 329 304 312 1950 312 300 317 292 300 311 345 350 344 336 315 323 1951 322 296 315 287 307 321 354 356 348 334 320 340 1952 332 302 324 305 318 329 359 363 359 352 335 342 1953 329 306 332 309 326 325 354 367 362 354 337 345 1954 339 325 345 309 315 334 370 383 375 370 344 355 1955 346 317 348 331 345 348 380 381 377 376 348 356 1956 344 320 347 326 343 338 361 368 378 374 347 358 1957 349 323 358 331 338 343 374 380 377 368 346 358 1958 338 329 347 327 335 336 370 399 385 368 351 362 1959 358 333 356 335 348 346 374 386 384 372 343 346 1960 346 318 359 328 333 329 366 373 367 363 337 346 1961 355 314 343 322 336 327 362 366 361 358 327 330 1962 336 326 337 316 331 331 359 350 356 347 328 336 1963 315 292 322 291 302 310 330 335 333 318 305 313 1964 301 281 302 291 297 291 311 319 317 317 296 307 1965 295 265 300 271 291 290 310 318 310 304 285 288 1966 277 260 282 274 288 287 308 312 306 304 282 305 1967 284 273 286 284 294 288 315 322 317 309 295 306 1968 300 275 301 292 298 306 326 332 329 328 308 324 1969 299 284 306 290 292 285 295 306 317 305 294 287 1970 278 261 275 256 270 264 265 284 284 275 269 275 1971 259 244 267 255 260 253 267 277 277 264 255 260 1972 261 238 257 246 254 255 273 276 286 283 261 276 1973 264 243 259 250 262 253 280 288 270 273 241 266 1974 257 242 266 241 252 250 281 278 286 278 260 272 1975 274 256 276 259 273 272 297 296 290 282 262 275 1976 262 251 285 260 272 265 296 312 289 282 274 281 1977 277```

# Plotting Time Series

Once you have read a time series into R, the next step is usually to make a plot of the time series data, which you can do with the plot.ts() function in R. To plot the time series of the monthly live births (adjusted) in thousands for the United States, we type:

`< plot.ts(birthtimeseries)`

We can see from this time series that there seems to be seasonal variation in the number of births per month: there is a peak every summer, and a trough every winter. Again, it seems that this time series could probably be described using an additive model, as the seasonal fluctuations are roughly constant in size over time and do not seem to depend on the level of the time series, and the random fluctuations also seem to be roughly constant in size over time.

# Decomposing Seasonal Data

A seasonal time series consists of a trend component, a seasonal component and an irregular component. Decomposing the time series means separating the time series into these three components: that is, estimating these three components.

To estimate the trend component and seasonal component of a seasonal time series that can be described using an additive model, we can use the “decompose()” function in R. This function estimates the trend, seasonal, and irregular components of a time series that can be described using an additive model.

The function “decompose()” returns a list object as its result, where the estimates of the seasonal component, trend component and irregular component are stored in named elements of that list objects, called “seasonal”, “trend”, and “random” respectively.

For example, as discussed above, the time series of the monthly live births (adjusted) in thousands for the United States is seasonal with a peak every summer and trough every winter, and can probably be described using an additive model since the seasonal and random fluctuations seem to be roughly constant in size over time:

`> birthtimeseriescomponents `

The estimated values of the seasonal, trend and irregular components are now stored in variables birthtimeseriescomponents\$seasonal, birthtimeseriescomponents\$trend and birthtimeseriescomponents\$random. For example, we can print out the estimated values of the seasonal component by typing:

```> birthtimeseriescomponents\$seasonal # get the estimated values of the seasonal component Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec 1946  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1947  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1948  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1949  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1950  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1951  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1952  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1953  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1954  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1955  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1956  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1957  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1958  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1959  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1960  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1961  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1962  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1963  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1964  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1965  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1966  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1967  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1968  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1969  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1970  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1971  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1972  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1973  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1974  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1975  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1976  -4.3 -25.1  -1.1 -21.6  -9.9  -9.2  16.5  23.6  20.2  13.8  -6.2   3.2 1977  -4.3```

The estimated seasonal factors are given for the months January-December, and are the same for each year. The largest seasonal factor is for August (about 23.6), and the lowest is for February (about -25.1), indicating that there seems to be a peak in births in July and a trough in births in February each year.

We can plot the estimated trend, seasonal, and irregular components of the time series by using the “plot()” function, for example:

`> plot(birthtimeseriescomponents)`

The plot below shows the original time series (top), the estimated trend component (second from top), the estimated seasonal component (third from top), and the estimated irregular component (bottom). We see that the estimated trend component shows a small increase from about 300 in 1947 to about 360 in 1959, followed by a steady decrease from then on to about 280 in 1966, followed by a slight increase to about 320 in 1969, followed by a steady decease to about 260 from about 1971 through 1974 and the a slight increase to about 280 in 1979.

If you have a seasonal time series that can be described using an additive model, you can seasonally adjust the time series by estimating the seasonal component, and subtracting the estimated seasonal component from the original time series. We can do this using the estimate of the seasonal component calculated by the “decompose()” function.

For example, to seasonally adjust the time series of the number of births per month in New York city, we can estimate the seasonal component using “decompose()”, and then subtract the seasonal component from the original time series:

`> birthtimeseriescomponents > birthtimeseriesseasonallyadjusted `

We can then plot the seasonally adjusted time series using the “plot()” function, by typing:

`> plot(birthtimeseriesseasonallyadjusted)`

You can see that the seasonal variation has been removed from the seasonally adjusted time series. The seasonally adjusted time series now just contains the trend component and an irregular component.

# Forecasts using Exponential Smoothing

Exponential smoothing can be used to make short-term forecasts for time series data.

## Simple Exponential Smoothing

If you have a time series that can be described using an additive model with constant level and no seasonality, you can use simple exponential smoothing to make short-term forecasts.

The simple exponential smoothing method provides a way of estimating the level at the current time point. Smoothing is controlled by the parameter alpha; for the estimate of the level at the current time point. The value of alpha; lies between 0 and 1. Values of alpha that are close to 0 mean that little weight is placed on the most recent observations when making forecasts of future values.

To make forecasts using simple exponential smoothing in R, we can fit a simple exponential smoothing predictive model using the “HoltWinters()” function in R. To use HoltWinters() for simple exponential smoothing, we need to set the parameters beta=FALSE and gamma=FALSE in the HoltWinters() function (the beta and gamma parameters are used for Holt’s exponential smoothing, or Holt-Winters exponential smoothing, as described below).

The HoltWinters() function returns a list variable, that contains several named elements.

For example, to use simple exponential smoothing to make forecasts for the time series of monthly live births (adjusted) in thousands for the United States, 1946-1979, we type:

`## Holt-Winters exponential smoothing without trend and without seasonal component.`
```> birthtimeseriesforecasts > birthtimeseriesforecasts Call: HoltWinters(x = birthtimeseries, beta = FALSE, gamma = FALSE) Smoothing parameters:   alpha: 0.71   beta : FALSE   gamma: FALSE Coefficients:   [,1] a  278```

The output of HoltWinters() tells us that the estimated value of the alpha parameter is about 0.71. This is very close to zero, telling us that the forecasts are based on both recent and less recent observations (although somewhat more weight is placed on recent observations).

By default, HoltWinters() just makes forecasts for the same time period covered by our original time series. In this case, our original time series included monthly live births (adjusted) in thousands for the United States, 1948-1979.

In the example above, we have stored the output of the HoltWinters() function in the list variable “birthstimeseriesforecasts”. The forecasts made by HoltWinters() are stored in a named element of this list variable called “fitted”, so we can get their values by typing:

```> birthtimeseriesforecasts \$fitted xhat level Feb 1946  295   295 Mar 1946  289   289 Apr 1946  297   297 May 1946  283   283 Jun 1946  275   275 Jul 1946  270   270 Aug 1946  297   297 Sep 1946  314   314 Oct 1946  313   313 Nov 1946  310   310 Dec 1946  296   296 Jan 1947  296   296 Feb 1947  295   295 Mar 1947  279   279 Apr 1947  294   294 May 1947  278   278 Jun 1947  281   281 Jul 1947  284   284 Aug 1947  308   308 Sep 1947  319   319 Oct 1947  315   315 Nov 1947  312   312 Dec 1947  297   297 . . . Jan 1976  273   273 Feb 1976  265   265 Mar 1976  255   255 Apr 1976  276   276 May 1976  265   265 Jun 1976  270   270 Jul 1976  266   266 Aug 1976  287   287 Sep 1976  305   305 Oct 1976  294   294 Nov 1976  285   285 Dec 1976  277   277 Jan 1977  280   280```

We can plot the original time series against the forecasts by typing:

`> plot(birthtimeseriesforecasts)`

The plot shows the original time series in black, and the forecasts as a red line. The time series of forecasts is much smoother than the time series of the original data here.

As a measure of the accuracy of the forecasts, we can calculate the sum of squared errors for the in-sample forecast errors, that is, the forecast errors for the time period covered by our original time series. The sum-of-squared-errors is stored in a named element of the list variable “birthtimeseriesforecasts” called “SSE”, so we can get its value by typing:

```> birthtimeseriesforecasts\$SSE [1] 97559```

That is, here the sum-of-squared-errors is 97559.

It is common in simple exponential smoothing to use the first value in the time series as the initial value for the level. For example, in the time series for monthly live births (adjusted) in thousands for the United States, 1946-1979, the first value is 295 in 1946. You can specify the initial value for the level in the HoltWinters() function by using the “l.start” parameter. For example, to make forecasts with the initial value of the level set to 295, we type:

`> HoltWinters(birthtimeseries, beta=FALSE, gamma=FALSE, l.start=295)`

As explained above, by default HoltWinters() just makes forecasts for the time period covered by the original data, which is 1946-1979 for the birth time series. We can make forecasts for further time points by using the “forecast.HoltWinters()” function in the R “forecast” package. To use the forecast.HoltWinters() function, we first need to install the “forecast” R package (for instructions on how to install an R package, see How to install an R package).

Once you have installed the “forecast” R package, you can load the “forecast” R package by typing:

`> library("forecast")`

When using the forecast.HoltWinters() function, as its first argument (input), you pass it the predictive model that you have already fitted using the HoltWinters() function. For example, in the case of the birth time series, we stored the predictive model made using HoltWinters() in the variable “birthstimeseriesforecasts”. You specify how many further time points you want to make forecasts for by using the “h” parameter in forecast.HoltWinters(). For example, to make a forecast of births for the years Feb 1977 to Sep 1978 (8 more months) using forecast.HoltWinters(), we type:

```> birthtimeseriesforecasts2 > birthtimeseriesforecasts2 Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 Feb 1977            278   257   299   246   310 Mar 1977            278   252   303   239   317 Apr 1977            278   248   307   233   323 May 1977            278   245   311   227   328 Jun 1977            278   242   314   223   333 Jul 1977            278   239   317   218   338 Aug 1978            278   236   320   214   342 Sep 1978            278   234   322   210   346```

The forecast.HoltWinters() function gives you the forecast for a year, a 80% prediction interval for the forecast, and a 95% prediction interval for the forecast. For example, the forecasted births for 1979 is about 275 births, with a 95% prediction interval of (250, 320).

To plot the predictions made by forecast.HoltWinters(), we can use the “plot.forecast()” function:

`> plot.forecast(birthtimeseriesforecasts2)`

Here the forecasts for 1946-1979 are plotted as a blue line, the 80% prediction interval as a gray shaded area, and the 95% prediction interval as a light gray shaded area.

The ‘forecast errors’ are calculated as the observed values minus predicted values, for each time point. We can only calculate the forecast errors for the time period covered by our original time series, which is 1946-1979 for the birth data. As mentioned above, one measure of the accuracy of the predictive model is the sum-of-squared-errors (SSE) for the in-sample forecast errors.

The in-sample forecast errors are stored in the named element “residuals” of the list variable returned by forecast.HoltWinters(). If the predictive model cannot be improved upon, there should be no correlations between forecast errors for successive predictions. In other words, if there are correlations between forecast errors for successive predictions, it is likely that the simple exponential smoothing forecasts could be improved upon by another forecasting technique.

To figure out whether this is the case, we can obtain a correlogram of the in-sample forecast errors for lags 1-20. We can calculate a correlogram of the forecast errors using the “acf()” function in R. To specify the maximum lag that we want to look at, we use the “lag.max” parameter in acf().

For example, to calculate a correlogram of the in-sample forecast errors for the birth data for lags 0-30, we type:

`> acf(birthtimeseriesforecasts2\$residuals, lag.max=30)`

You can see from the sample correlogram that the autocorrelation at lag 0 is just crosses the significance bounds. To test whether there is significant evidence for non-zero correlations at lags 1-30, we can carry out a Ljung-Box test. This can be done in R using the “Box.test()”, function. The maximum lag that we want to look at is specified using the “lag” parameter in the Box.test() function. For example, to test whether there are non-zero autocorrelations at lags 1-30, for the in-sample forecast errors for monthly live births for the United States data (1946-1977), we type:

`> Box.test(birthtimeseriesforecasts2\$residuals, lag=20, type="Ljung-Box")`
```          Box-Ljung test data:  birthtimeseriesforecasts2\$residuals X-squared = 470, df = 20, p-value < 2.2e-16```

Here the Ljung-Box test statistic is 470, and the p-value is < 0.001, so there is evidence of non-zero autocorrelations in the in-sample forecast errors at lags 1-30.

To be sure that the predictive model cannot be improved upon, it is also a good idea to check whether the forecast errors are normally distributed with mean zero and constant variance. To check whether the forecast errors have constant variance, we can make a time plot of the in-sample forecast errors:

`> plot.ts(birthtimeseriesforecasts2\$residuals)`

The plot shows that the in-sample forecast errors seem to have roughly constant variance over time, although the size of the fluctuations in the start of the time series (1846-1861) may be slightly less than that at later dates (e.g., 1862-1877).

To check whether the forecast errors are normally distributed with mean zero, we can plot a histogram of the forecast errors, with an overlaid normal curve that has mean zero and the same standard deviation as the distribution of forecast errors. To do this, we can define an R function “plotForecastErrors()”, below:

`> plotForecastErrors function(forecasterrors) `
`{ `
` # make a histogram of the forecast errors:`
```   mybinsize    mysd    mymin    mymax    # generate normally distributed data with mean 0 and standard deviation mysd    mynorm    mymin2    mymax2       if (mymin2 < mymin) { mymin       if (mymax2 > mymax) { mymax    # make a red histogram of the forecast errors, with the normally distributed data overlaid:    mybins    hist(forecasterrors, col="red", freq=FALSE, breaks=mybins)    # freq=FALSE ensures the area under the histogram = 1    # generate normally distributed data with mean 0 and standard deviation mysd    myhist FALSE, breaks=mybins)      # plot the normal curve as a blue line on top of the histogram of forecast errors:    points(myhist\$mids, myhist\$density, type="l", col="blue", lwd=2) }```

You will have to copy the function above into R in order to use it. You can then use plotForecastErrors() to plot a histogram (with overlaid normal curve) of the forecast errors for the birth predictions:

`> plotForecastErrors(birthtimeseriesforecasts2\$residuals)`

The plot shows that the distribution of forecast errors is roughly centered on zero, and is more or less normally distributed, although it seems to be slightly skewed to the right compared to a normal curve. However, the right skew is relatively small, and so it is plausible that the forecast errors are normally distributed with mean zero.

The Ljung-Box test showed that there is little evidence of non-zero autocorrelations in the in-sample forecast errors, and the distribution of forecast errors seems to be normally distributed with mean zero. This suggests that the simple exponential smoothing method provides an adequate predictive model for the births for the United States data (1946-1977), which probably cannot be improved upon. Furthermore, the assumptions that the 80% and 95% predictions intervals were based upon (that there are no autocorrelations in the forecast errors, and the forecast errors are normally distributed with mean zero and constant variance) are probably valid.

Authored by:
Jeffrey Strickland, Ph.D.

Jeffrey Strickland, Ph.D., is the Author of “Predictive Analytics Using R” and a Senior Analytics Scientist with Clarity Solution Group. He has performed predictive modeling, simulation and analysis for the Department of Defense, NASA, the Missile Defense Agency, and the Financial and Insurance Industries for over 20 years. Jeff is a Certified Modeling and Simulation professional (CMSP) and an Associate Systems Engineering Professional. He has published nearly 200 blogs on LinkedIn, is also a frequently invited guest speaker and the author of 20 books including:

• Operations Research using Open-Source Tools
• Discrete Event simulation using ExtendSim
• Crime Analysis and Mapping
• Missile Flight Simulation
• Mathematical Modeling of Warfare and Combat Phenomenon
• Predictive Modeling and Analytics
• Using Math to Defeat the Enemy
• Verification and Validation for Modeling and Simulation
• Simulation Conceptual Modeling
• System Engineering Process and Practices
• Weird Scientist: the Creators of Quantum Physics
• Albert Einstein: No one expected me to lay a golden eggs
• The Men of Manhattan: the Creators of the Nuclear Era
• Fundamentals of Combat Modeling