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**Time series modelling and statistical trends**

Marian Scott and Adrian Bowman SEPA, July 2012

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**smoothing a time series**

In many time series, the seasonal variation can be so strong that it obscures any trend or cyclical component. for understanding the process being observed (and forecasting future values of the series), trends and cycles are of prime importance. Smoothing is a process designed to remove seasonality so that the long-term movements in a time series can be seen more clearly

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**smoothing a time series**

one of the most commonly used smoothing techniques is moving average. difficult choice: the window over which to smooth smooth series: Yi = wkYi+k other smoothing methods (more modern) commonly used include Lowess

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**smoothing a time series**

We have data , where Xt = number of bus passengers on the t'th day. Since the periodic variation is repeated every 7 days, a 7-period moving-average (Mt) is used to smooth the series, where: Mt = 1/7{Xt-3+Xt-2+…..+Xt+3} This averages out the seasonality, since each Mt is an average over 7 different 'seasons' (days of the week). Note, though, that Mt is only defined for t = 4, 5, ..., N-7. other smoothing methods (more modern) commonly used include Lowess

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**smoothing a time series**

one of the most commonly used smoothing techniques is moving average. smooth series: Yi = wkYi+k 3-point, 5-point, 7-point moving average example window may be chosen to reflect the periodicity of the data series other smoothing methods (more modern) commonly used include Lowess

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**smoothing a time series**

LO(W)ESS, is a method that is known as locally weighted polynomial regression. At each point in the data set a low-degree polynomial is fit to a subset of the data, with explanatory variable values near the point whose response is being estimated. The polynomial is fit using weighted least squares, giving more weight to points near the point whose response is being estimated and less weight to points further away. Many of the details of this method, such as the degree of the polynomial model and the weights, are flexible.

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**water surface temperature from Jan 1981- Feb 1992 (Piegorsch)- with lowess curve**

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**Example : different smoothing technique applied to air quality data (that have been logged)**

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harmonic regression another way of a) describing and b) hence being able to remove the periodic component is to use what is called harmonic regression remember sin and cos from school? This allows us to capture the regular repeat pattern in each year – the seasonal effect

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**Yi = 0 + cos (2[ti - ]/p) + i**

harmonic regression build a regression model using the sine function. sin () lies between -1 and +1, where measured in radians. for a periodic time series Yi we can build a regression model Yi = 0 + cos (2[ti - ]/p) + i to make this simpler, if we assume that p is known, this can be written as a simple multiple linear regression model

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**Yi = 0 + sin (2[ti - ]/p) + i**

harmonic regression for a periodic time series Yi we can build a regression model Yi = 0 + sin (2[ti - ]/p) + i to make this simpler, Yi = 0 + 1ci + 2si + i where ci = cos(2ti/p) and si = sin(2ti/p) So a regression model

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**Example: red curve shows the harmonic pattern (superimposed on a declining trend).**

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**Example to try Qn 4 in practical3final.txt**

The script shows how we can create the new explanatory variables, doy is a new variable that records where in the year (which day from 366) the sample was taken.

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**seasonal indices and de-seasonalisation**

The reason for giving the seasonally-adjusted data is to make trends and cycles more apparent. seasonal adjustments best explained as step 1: define the Yt =Xt –Mt (actual-smoothed) step 2: average all the Yt values for each ‘season’ to give the same seasonal index (e.g. for quarterly data there would be 4 values), S step 3: the seasonally adjusted data Xt- S

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**correlation through time**

in many situations, we expect successive observations to show correlation at adjacent time points (most likely stronger the closer the time points are), strength of dependence usually depends on time separation or lag for regularly spaced data, we typically make use of the autocorrelation function (ACF) Data are NOT independent

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**correlation through time**

for regularly spaced time series, with no missing data, we define the sample mean in the usual way then the sample autocorrelation coefficient at lag k ( 0), r(k)- as the correlation between original series and a version shifted back k time units horizontal lines show approximate 95% confidence intervals for individual coefficients.

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**Example: ACF of raw water temperature data**

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**correlation through time**

ACF shows a very marked cyclical pattern interpretation of the ACF we need to have removed both trend and seasonality we hope that (for simplicity in subsequent modelling) that only a few correlation coefficients (at small lags) will be significant. ACF an important diagnostic tool for time series modelling (formal models called ARIMA). how should we remove the seasonal pattern or the trend?

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differencing a common way of removing a simple trend (eg linear) is by differencing define a new series Zt = Yt – Yt-1 a common way of removing seasonality (if we know the period to be p), is to take pth differences Zt = Yt – Yt-p

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**Example: ACF of water temperature data**

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**Example 1: ACF of water temperature data- difference order 12**

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**Examples to try In practical4.txt Exercises 1 and 2**

Why is correlation important How good is the ACF as a diagnostic Exercise 2 shows the output from a single command stl (which is a decomposition of the data series into trend, seasonal component and residual)

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simple algorithm obtain rough estimate of trend (smoothing but one not affected by seasonality): subtract estimated trend estimate seasonal cycle from detrended series what is left is the irregular component, good alternative- STL (seasonal trend lowess) decomposition

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**An example for you to try**

Exercise 3, Central England temperature obtain the acf use the stl() command Look at monthly data

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**A different type of change**

Change can be Abrupt As a result of an intervention So we might like to consider a slightly different form of model

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Nile flow relatively poor fit of straight line model, lots of variation. some pattern in the residuals

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**A straight line model for the Nile**

relatively poor fit, lot of variation. any pattern in the residuals? this residual plot is against order of the observations

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**a non-parametric model for the Nile**

a smooth function (LOESS) or non-parametric regression model Seem OK? any suggestion that there may be a change-point?

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**A different type of change**

So we might like to consider a slightly different form of model- the river Nile was dammed in the late 1800s So there may be a changepoint- a shift in the mean flow level, and if so can we see it.

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the smooths Two smooth curves are fit and we identify the biggest discrepancy between then with confidence bands added, helps identify the change location Where is the biggest discrepancy?

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**An alternative model for the Nile**

two smooth sections, broken at roughly 1900. different mean levels in the two periods so modelling the two periods separately

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**The moral of this example**

Trends can be challenging to identify Modelling needs to be flexible We need to be mindful of the assumptions

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An example Haddocks- this is an example about fish stocks, we can try fitting some very simple time series regression models. We might want to predict what fish stocks might be several years in the future

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