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Linear trend estimation

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Linear trend estimation is a statistical technique used to analyze data patterns. Data patterns, or trends, occur when the information gathered "tends" to increase or decrease over time. Linear trend estimation essentially creates a straight line on a graph of data that models the general direction that the data is heading.

Fitting a trend: Least-squares[edit]

Given a set of data, there are a variety of functions that can be chosen for the fit. The simplest function is a straight line with the data values on the vertical axis and time (t = 1, 2, 3, ...) on the horizontal axis.

The least-squares fit is a common method to fit a straight line through the data. This method minimizes the sum of the squared errors in the data series y. Given a set of points in time and data values observed for those points in time, values of and are chosen to minimize the sum of squared errors.

The values of and derived from the data parameterize the simple linear estimator . The term "trend" refers to the slope in the least squares estimator.

Trends in random data[edit]

Red shaded values are greater than 99% of the rest; blue, 95%; green, 90%. In this case, the V values discussed in the text for (one-sided) 95% confidence is seen to be 0.2.

If a series that is known to be random is analyzed – fair dice falls or computer-generated pseudo-random numbers – and a trend line is fitted through the data, the chances of an exactly zero estimated trend are negligible. But the trend would be expected to be small. If an individual series of observations is generated from simulations that employ a given variance of noise that equals the observed variance of our data series of interest, and a given length (say, 100 points), a large number of such simulated series (say, 100,000 series) can be generated. These 100,000 series can then be analyzed individually to calculate estimated trends in each series, and these results establish a distribution of estimated trends that are to be expected from such random data (see diagram). Such a distribution will be normal according to the central limit theorem, except in pathological cases. A level of statistical certainty, S, may now be selected: 95% confidence is typical; 99% would be stricter, 90% looser. And the following question can be asked: what is the borderline trend value V that would result in S% of trends being between −V and +V?

The above procedure can be replaced by a permutation test. To generate borderline trend values V and −V, the set of 100,000 generated series can be replaced by 100,000 series constructed by randomly shuffling the observed data series. Since such a constructed series would be trend-free, it can be used similarly to simulated data.

The distribution of trends was calculated by simulation in the above discussion. In simple cases, such as normally distributed random noise, the distribution of trends can be calculated exactly without simulation.

The range (−V, V) can be used to decide whether a trend estimated from the actual data is unlikely to have come from a data series that truly has a zero trend. If the estimated value of the regression parameter lies outside this range, such a result could have occurred in the presence of a true zero trend only, for example, one time out of twenty if the confidence value S = 95% was used. In this case, it can be said that, at the degree of certainty S, we reject the null hypothesis that the true underlying trend is zero.

However, note that whatever value of S is chosen, 1-S is declared, and a truly random series will be concluded (falsely, by construction) to have a significant trend. Conversely, a certain fraction of series that have a non-zero trend will not be declared to have a trend.

Data as trend and noise[edit]

To analyze a (time) series of data, it can be assumed that it may be represented as trend plus noise:

where and are unknown constants and the 's are randomly distributed errors. If one can reject the null hypothesis that the errors are non-stationary, then the non-stationary series {yt } is called trend-stationary. The least squares method assumes the errors are independently distributed with a normal distribution. If this is not the case, hypothesis tests about the unknown parameters a and b may be inaccurate. It is simplest if the 's all have the same distribution, but if not (if some have higher variance, meaning that those data points are effectively less certain), then this can be taken into account during the least squares fitting, by weighting each point by the inverse of the variance of that point.

Commonly, where only a single time series exists to be analyzed, the variance of the 's is estimated by fitting a trend to obtain the estimated parameter values and thus allowing the predicted values

to be subtracted from the data (thus detrending the data), and leaving the residuals as the detrended data, and estimating the variance of the 's from the residuals — this is often the only way of estimating the variance of the 's.

Once the "noise" of the series is known, the significance of the trend can be assessed by making the null hypothesis that the trend, , is not different from 0. From the above discussion of trends in random data with known variance, the distribution of calculated trends is to be expected from random (trendless) data. If the estimated trend, , is larger than the critical value for a certain significance level, then the estimated trend is deemed significantly different from zero at that significance level, and the null hypothesis of a zero underlying trend is rejected.

The use of a linear trend line has been the subject of criticism, leading to a search for alternative approaches to avoid its use in model estimation. One of the alternative approaches involves unit root tests and the cointegration technique in econometric studies.

The estimated coefficient associated with a linear trend variable such as time is interpreted as a measure of the impact of a number of unknown or known but immeasurable factors on the dependent variable over one unit of time. Strictly speaking, this interpretation is applicable for the estimation time frame only. Outside of this time frame, it cannot be determined how these immeasurable factors behave both qualitatively and quantitatively.

Research results by mathematicians, statisticians, econometricians, and economists have been published in response to those questions. For example, detailed notes on the meaning of linear time trends in the regression model are given in Cameron (2005);[1] Granger, Engle, and many other econometricians have written on stationarity, unit root testing, co-integration, and related issues (a summary of some of the works in this area can be found in an information paper[2] by the Royal Swedish Academy of Sciences (2003); and Ho-Trieu & Tucker (1990) have written on logarithmic time trends with results indicating linear time trends are special cases of cycles.

Noisy time series[edit]

It is harder to see a trend in a noisy time series. For example, if the true series is 0, 1, 2, 3, all plus some independent normally distributed "noise" e of standard deviation E, and a sample series of length 50 is given, then if E = 0.1, the trend will be obvious; if E = 100, the trend will probably be visible; but if E = 10000, the trend will be buried in the noise.

Consider a concrete example, such as the global surface temperature record of the past 140 years as presented by the IPCC.[3] The interannual variation is about 0.2 °C, and the trend is about 0.6 °C over 140 years, with 95% confidence limits of 0.2 °C (by coincidence, about the same value as the interannual variation). Hence, the trend is statistically different from 0. However, as noted elsewhere.[4] This time series doesn't conform to the assumptions necessary for least squares to be valid.

Goodness of fit (r-squared) and trend[edit]

Illustration of the effect of filtering on r2. Black = unfiltered data; red = data averaged every 10 points; blue = data averaged every 100 points. All have the same trend, but more filtering leads to higher r2 of fitted trend line.

The least-squares fitting process produces a value, r-squared (r2), which is 1 minus the ratio of the variance of the residuals to the variance of the dependent variable. It says what fraction of the variance of the data is explained by the fitted trend line. It does not relate to the statistical significance of the trend line (see graph); the statistical significance of the trend is determined by its t-statistic. Often, filtering a series increases r2 while making little difference to the fitted trend.

Advanced models[edit]

Thus far, the data have been assumed to consist of the trend plus noise, with the noise at each data point being independent and identically distributed random variables with a normal distribution. Real data (for example, climate data) may not fulfill these criteria. This is important, as it makes an enormous difference to the ease with which the statistics can be analyzed so as to extract maximum information from the data series. If there are other non-linear effects that have a correlation to the independent variable (such as cyclic influences), the use of least-squares estimation of the trend is not valid. Also, where the variations are significantly larger than the resulting straight line trend, the choice of start and end points can significantly change the result. That is, the model is mathematically misspecified. Statistical inferences (tests for the presence of a trend, confidence intervals for the trend, etc.) are invalid unless departures from the standard assumptions are properly accounted for, for example, as follows:

In R, the linear trend in data can be estimated by using the 'tslm' function of the 'forecast' package.

Trends in clinical data[edit]

Medical and biomedical studies often seek to determine a link between sets of data, such as (as indicated above) three different diseases. But data may also be linked in time (such as change in the effect of a drug from baseline, to month 1, to month 2), or by an external factor that may or may not be determined by the researcher and/or their subject (such as no pain, mild pain, moderate pain, or severe pain). In these cases, one would expect the effect test statistic (e.g., influence of a statin on levels of cholesterol, an analgesic on the degree of pain, or increasing doses of a drug on a measurable index) to change in direct order as the effect develops. Suppose the mean level of cholesterol before and after the prescription of a statin falls from 5.6 mmol/L at baseline to 3.4 mmol/L at one month and to 3.7 mmol/L at two months. Given sufficient power, an ANOVA (analysis of variance) would most likely find a significant fall at one and two months, but the fall is not linear. Furthermore, a post-hoc test may be required. An alternative test may be a repeated measures (two way) ANOVA or Friedman test, depending on the nature of the data. Nevertheless, because the groups are ordered, a standard ANOVA is inappropriate. Should the cholesterol fall from 5.4 to 4.1 to 3.7, there is a clear linear trend. The same principle may be applied to the effects of allele/genotype frequency, where it could be argued that SNPs in nucleotides XX, XY, YY are in fact a trend of no Y's, one Y, and then two Y's.[3]

The mathematics of linear trend estimation is a variant of the standard ANOVA, giving different information, and would be the most appropriate test if the researchers hypothesize a trend effect in their test statistic. One example is of levels of serum trypsin in six groups of subjects ordered by age decade (10–19 years up to 60–69 years). Levels of trypsin (ng/mL) rise in a direct linear trend of 128, 152, 194, 207, 215, 218. Unsurprisingly, a 'standard' ANOVA gives p < 0.0001, whereas linear trend estimation give p = 0.00006. Incidentally, it could be reasonably argued that as age is a natural continuously variable index, it should not be categorized into decades, and an effect of age and serum trypsin is sought by correlation (assuming the raw data is available). A further example is of a substance measured at four time points in different groups:

# mean SD
1 1.6 0.56
2 1.94 0.75
3 2.22 0.66
4 2.40 0.79

This is a clear trend. ANOVA gives p = 0.091, because the overall variance exceeds the means, whereas linear trend estimation gives p = 0.012. However, should the data have been collected at four time points in the same individuals, linear trend estimation would be inappropriate, and a two-way (repeated measures) ANOVA would have been applied.

See also[edit]


  1. ^ "Making Regression More Useful II: Dummies and Trends" (PDF). Retrieved June 17, 2012.
  2. ^ "The Royal Swedish Academy of Sciences" (PDF). 8 October 2003. Retrieved June 17, 2012.
  3. ^ a b "IPCC Third Assessment Report – Climate Change 2001 – Complete online versions". Archived from the original on November 20, 2009. Retrieved June 17, 2012.
  4. ^ a b Forecasting: principles and practice. 20 September 2014. Retrieved May 17, 2015.