to ARIMA: nonseasonal models
Identifying the order of differencing in an ARIMA model
Identifying the numbers of AR or MA terms in an ARIMA model
Estimation of ARIMA models
Seasonal differencing in ARIMA models
Seasonal random walk: ARIMA(0,0,0)x(0,1,0)
Seasonal random trend: ARIMA(0,1,0)x(0,1,0)
General seasonal models: ARIMA (0,1,1)x(0,1,1) etc.
Summary of rules for identifying ARIMA models
ARIMA models with regressors
The mathematical structure of ARIMA models (pdf file)
Outline of seasonal ARIMA modeling
Example: AUTOSALE series revisited
The often-used ARIMA(0,1,1)x(0,1,1) model: SRT model plus MA(1) and SMA(1) terms
The ARIMA(1,0,0)x(0,1,0) model with constant: SRW model plus AR(1) term
An improved version: ARIMA(1,0,1)x(0,1,1) with constant
Seasonal ARIMA versus exponential smoothing and seasonal adjustment
What are the tradeoffs among the various seasonal models?
To log or not to log?
Excel file with auto sales data
Statgraphics data and model files (zip)
Recall that we previously forecast the retail auto sales series by using a combination of deflation, seasonal adjustment and exponential smoothing. Let's now try fitting the same series with seasonal ARIMA models, using the same sample of data from January 1970 to May 1993 (281 observations). As before we will work with deflated auto sales--i.e., we will use the series AUTOSALE/CPI as the input variable. Here are the time series plot and ACF and PACF plots of the original series, which are obtained in the Forecasting procedure by plotting the "residuals" of an ARIMA(0,0,0)x(0,0,0) model with constant:
The "suspension bridge" pattern in the ACF is typical of a series that is both nonstationary and strongly seasonal. Clearly we need at least one order of differencing. If we take a nonseasonal difference, the corresponding plots are as follows:
The differenced series (the residuals of a random-walk-with-growth model) looks more-or-less stationary, but there is still very strong autocorrelation at the seasonal period (lag 12).
Because the seasonal pattern is strong and stable, we know (from Rule 12) that we will want to use an order of seasonal differencing in the model. Here is what the picture looks like after a seasonal difference (only):
The seasonally differenced series shows a very strong pattern of positive autocorrelation, as we recall from our earlier attempt to fit a seasonal random walk model. This could be an "AR signature"--or it could signal the need for another difference.
If we take both a seasonal and nonseasonal difference, following results are obtained:
These are, of course, the residuals from the seasonal random trend model that we fitted to the auto sales data earlier. We now see the telltale signs of mild overdifferencing: the positive spikes in the ACF and PACF have become negative.
What is the correct order of differencing? One more piece of information that might be helpful is a calculation of the error statistics of the series at each level of differencing. We can compute these by fitting the corresponding ARIMA models in which only differencing is used:
The smallest errors, in both the estimation period and validation period, are obtained by model A, which uses one difference of each type. This, together with the appearance of the plots above, strongly suggests that we should use both a seasonal and a nonseasonal difference. Note that, except for the gratuitious constant term, model A is the seasonal random trend (SRT) model, whereas model B is just the seasonal random walk (SRW) model. As we noted earlier when comparing these models, the SRT model appears to fit better than the SRW model. In the analysis that follows, we will try to improve these models through the addition of seasonal ARIMA terms. Return to top of page.
Returning to the last set of plots above, notice that with one difference of each type there is a negative spike in the ACF at lag 1 and also a negative spike in the ACF at lag 12, whereas the PACF shows a more gradual "decay" pattern in the vicinity of both these lags. By applying our rules for identifying ARIMA models (specifically, Rule 7 and Rule 13), we may now conclude that the SRT model would be improved by the addition of an MA(1) term and also an SMA(1) term. Also, by Rule 5, we exclude the constant since two orders of differencing are involved. If we do all this, we obtain the ARIMA(0,1,1)x(0,1,1) model, which is the most commonly used seasonal ARIMA model. Its forecasting equation is:
Ŷt = Yt-12 + Yt-1 – Yt-13 - θ1et-1 – Θ1et-12 + θ1Θ1et-13
where θ1 is the MA(1) coefficient and Θ1 (capital theta-1) is the SMA(1) coefficient. Notice that this is just the seasonal random trend model fancied-up by adding multiples of the errors at lags 1, 12, and 13. Also, notice that the coefficient of the lag-13 error is the product of the MA(1) and SMA(1) coefficients. This model is conceptually similar to the Winters model insofar as it effectively applies exponential smoothing to level, trend, and seasonality all at once, although it rests on more solid theoretical foundations, particularly with regard to calculating confidence intervals for long-term forecasts.
Its residual plots in this case are as follows:
Although a slight amount of autocorrelation remains at lag 12, the overall appearance of the plots is good. The model fitting results show that the estimated MA(1) and SMA(1) coefficients (obtained after 7 iterations) are indeed significant:
The forecasts from the model resemble those of the seasonal random trend model--i.e., they pick up the seasonal pattern and the local trend at the end of the series--but they are slightly smoother in appearance since both the seasonal pattern and the trend are effectively being averaged (in a exponential-smoothing kind of way) over the last few seasons:
What is this model really doing? You can think of it in the following way. First it computes the difference between each month’s value and an “exponentially weighted historical average” for that month that is computed by applying exponential smoothing to values that were observed in the same month in previous years, where the amount of smoothing is determined by the SMA(1) coefficient. Then it applies simple exponential smoothing to these differences in order to predict the deviation from the historical average that will be observed next month. The value of the SMA(1) coefficient near 1.0 suggests that many seasons of data are being used to calculate the historical average for a given month of the year. Recall that an MA(1) coefficient in an ARIMA(0,1,1) model corresponds to 1-minus-alpha in the corresponding exponential smoothing model, and that the average age of the data in an exponential smoothing model forecast is 1/alpha. The SMA(1) coefficient has a similar interpretation with respect to averages across seasons. Here its value of 0.91 suggests that the average age of the data used for estimating the historical seasonal pattern is a little more than 10 years (nearly half the length of the data set), which means that an almost constant seasonal pattern is being assumed. The much smaller value of 0.5 for the MA(1) coefficient suggests that relatively little smoothing is being done to estimate the current deviation from the historical average for the same month, so next month’s predicted deviation from its historical average will be close to the deviations from the historical average that were observed over the last few months.
The previous model was a Seasonal Random Trend (SRT) model fine-tuned by the addition of MA(1) and SMA(1) coefficients. An alternative ARIMA model for this series can be obtained by substituting an AR(1) term for the nonseasonal difference--i.e., by adding an AR(1) term to the Seasonal Random Walk (SRW) model. This will allow us to preserve the seasonal pattern in the model while lowering the total amount of differencing, thereby increasing the stability of the trend projections if desired. (Recall that with one seasonal difference alone, the series did show a strong AR(1) signature.) If we do this, we obtain an ARIMA(1,0,0)x(0,1,0) model with constant, which yields the following results:
The AR(1) coefficient is indeed highly significant, and the RMSE is only 2.06, compared to 3.00 for the SRW model (Model B in the comparison report above). The forecasting equation for this model is:
Ŷt = μ + Yt-12 + ϕ1(Yt-1 - Yt-13)
The additional term on the right-hand-side is a multiple of the seasonal difference observed in the last month, which has the effect of correcting the forecast for the effect of an unusually good or bad year. Here ϕ1 denotes the AR(1) coefficient, whose estimated value is 0.73. Thus, for example, if sales last month were X dollars ahead of sales one year earlier, then the quantity 0.73X would be added to the forecast for this month. μ denotes the CONSTANT in the forecasting equation, whose estimated value is 0.20. The estimated MEAN, whose value is 0.75, is the mean value of the seasonally differenced series, which is the annual trend in the long-term forecasts of this model. The constant is (by definition) equal to the mean times 1 minus the AR(1) coefficient: 0.2 = 0.75*(1 – 0.73).
The forecast plot shows that the model indeed does a better job than the SRW model of tracking cyclical changes (i.e., unusually good or bad years):
However, the MSE for this model is still significantly larger than what we obtained for the ARIMA(0,1,1)x(0,1,1) model. If we look at the plots of residuals, we see room for improvement. The residuals still show some sign of cyclical variation:
The ACF and PACF suggest the need for both MA(1) and SMA(1) coefficients:
If we add the indicated MA(1) and SMA(1) terms to the preceding model, we obtain an ARIMA(1,0,1)x(0,1,1) model with constant, whose forecasting equation is
Ŷt = μ + Yt-12 + ϕ1(Yt-1 – Yt-13) - θ1et-1 – Θ1et-12 + θ1Θ1et-13
This is nearly the same as the ARIMA(0,1,1)x(0,1,1) model except that it replaces the nonseasonal difference with an AR(1) term (a "partial difference") and it incorporates a constant term representing the long-term trend. Hence, this model assumes a more stable trend than the ARIMA(0,1,1)x(0,1,1) model, and that is the principal difference between them.
The model-fitting results are as follows:
Notice that the estimated AR(1) coefficient (ϕ1 in the model equation) is 0.96, which is very close to 1.0 but not so close as to suggest that it absolutely ought to be replaced with a first difference: its standard error is 0.02, so it is about 2 standard errors from 1.0. The other statistics of the model (the estimated MA(1) and SMA(1) coefficients and error statistics in the estimation and validation periods) are otherwise nearly identical to those of the ARIMA(0,1,1)x(0,1,1) model. (The estimated MA(1) and SMA(1) coefficients are 0.45 and 0.91 in this model vs. 0.48 and 0.91 in the other.)
The estimated MEAN of 0.68 is the predicted long-term trend (average annual increase). This is essentially the same value that was obtained in the (1,0,0)x(0,1,0)-with-constant model. The standard error of the estimated mean is 0.26, so the difference between 0.75 and 0.68 is not significant.
If the constant was not included in this model, it would be a damped-trend model: the trend in its very-long-term forecasts would gradually flatten out.
The point forecasts from this model look quite similar to those of the (0,1,1)x(0,1,1) model, because the average trend is similar to the local trend at the end of the series. However, the confidence intervals for this model widen somewhat less rapidly because of its assumption that the trend is stable. Notice that the confidence limits for the two-year-ahead forecasts now stay within the horizontal grid lines at 24 and 44, whereas those of the (0,1,1)x(0,1,1) model did not:
Seasonal ARIMA versus exponential smoothing and seasonal adjustment: Now let's compare the performance the two best ARIMA models against simple and linear exponential smoothing models accompanied by multiplicative seasonal adjustment, and the Winters model, as shown in the slides on forecasting with seasonal adjustment:
The error statistics for the one-period-ahead forecasts for all the models are extremely close in this case. It is hard to pick a “winner” based on these numbers alone. Return to top of page.
What are the tradeoffs among the various seasonal models? The three models that use multiplicative seasonal adjustment deal with seasonality in an explicit fashion--i.e., seasonal indices are broken out as an explicit part of the model. The ARIMA models deal with seasonality in a more implicit manner--we can't easily see in the ARIMA output how the average December, say, differs from the average July. Depending on whether it is deemed important to isolate the seasonal pattern, this might be a factor in choosing among models. The ARIMA models have the advantage that, once they have been initialized, they have fewer "moving parts" than the exponential smoothing and adjustment models and as such they may be less likely to overfit the data. ARIMA models also have a more solid underlying theory with respect to the calculation of confidence intervals for longer-horizon forecasts than do the other models.
There are more dramatic differences among the models with respect to the behavior of their forecasts and confidence intervals for forecasts more than 1 period into the future. This is where the assumptions that are made with respect to changes in the trend and seasonal pattern are very important.
Between the two ARIMA models, one (model A) estimates a time-varying trend, while the other (model B) incorporates a long-term average trend. (We could, if we desired, flatten out the long-term trend in model B by suppressing the constant term.) Among the exponential-smoothing-plus-adjustment models, one (model C) assumes a flat trend, while the other (model D) assumes a time-varying trend. The Winters model (E) also assumes a time-varying trend.
Models that assume a constant trend are relatively more confident in their long-term forecasts than models that do not, and this will usually be reflected in the extent to which confidence intervals for forecasts get wider at longer forecast horizons. Models that do not assume time-varying trends generally have narrower confidence intervals for longer-horizon forecasts, but narrower is not better unless this assumption is correct.
The two exponential smoothing models combined with seasonal adjustment assume that the seasonal pattern has remained constant over the 23 years in the data sample, while the other three models do not. Insofar as the seasonal pattern accounts for most of the month-to-month variation in the data, getting it right is important for forecasting what will happen several months into the future. If the seasonal pattern is believed to have changed slowly over time, another approach would be to just use a shorter data history for fitting the models that estimate fixed seasonal indices.
For the record, here are the forecasts and 95% confidence limits for May 1995 (24 months ahead) that are produced by the five models:
The point forecasts are actually surprisingly close to each other, relative to the widths of all the confidence intervals. The SES point forecast is the lowest, because it is the only model that does not assume an upward trend at the end of the series. The ARIMA (1,0,1)x(0,1,1)+c model has the narrowest confidence limits, because it assumes less time-variation in the parameters than the other models. Also, its point forecast is slightly larger than those of the other models, because it is extrapolating a long-term trend rather than a short-term trend (or zero trend).
The Winters model is the least stable of the models and its forecast therefore has the widest confidence limits, as was apparent in the detailed forecast plots for the models. And the forecasts and confidence limits of the ARIMA(0,1,1)x(0,1,1) model and those of the LES+seasonal adjustment model are virtually identical!
To log or not to log? Something that we have not yet done, but might have, is include a log transformation as part of the model. Seasonal ARIMA models are inherently additive models, so if we want to capture a multiplicative seasonal pattern, we must do so by logging the data prior to fitting the ARIMA model. (In Statgraphics, we would just have to specify "Natural Log" as a modeling option--no big deal.) In this case, the deflation transformation seems to have done a satisfactory job of stabilizing the amplitudes of the seasonal cycles, so there does not appear to be a compelling reason to add a log transformation as far as long term trends are concerned. If the residuals showed a marked increase in variance over time, we might decide otherwise.
There is still a question of whether the errors of these models have a consistent variance across months of the year. If they don’t, then confidence intervals for forecasts might tend to be too wide or too narrow according to the season. The residual-vs-time plots do not show an obvious problem in this regard, but to be thorough, it would be good to look at the error variance by month. If there is indeed a problem, a log transformation might fix it. Return to top of page.