TREND requires a continuous time series as input data.

Various time series data formats are supported within the TIME environment.

After downloading TREND to the default directory, sample data file can be found in c:\Program Files\Toolkit\TREND\Data.

TREND performs the statistical tests on annual time series data. TREND will convert daily and monthly time series data into an annual time series before carrying out the statistical testing on the annual time series (see the TREND User Guide on how you can modify the data file to use TREND for non-annual time series data). Input data files must be continuous and complete (daily and monthly data files must have complete years of records).

File Formats

More information about file formats supported by this application can be found in the Toolkit Data Files guide.

TREND displays as an output (for all the statistical tests selected by the user):

• Value of the test statistic
• Critical values of the test statistic for a = 0.01, a = 0.05 and a = 0.1 significance levels (from statistical table)
• Critical values of the test statistic for a = 0.01, a = 0.05 and a = 0.1 significance levels (from resampling analysis)
• Simple statement of test result

TREND also provides a graphical display of the time series data.

TREND also allows easy retrieval of the test results.

TREND performs the statistical tests only on annual time series data (see the TREND User Guide on how you can modify the data file to use TREND for non-annual time series data).

The statistical tests in TREND are only valid if the time series data is not serially correlated. Most (but not all) time series data with time steps shorter than the annual time step are serially correlated. The Autocorrelation test (one of the tests in TREND) can be used to test if the time series data is serially correlated. Users should also have a good understanding of the statistical tests and other assumptions before using TREND (see User Guide).

Users are strongly encouraged to carry out an exploratory data analysis (EDA) before using TREND. EDA involves using graphs to explore, understand and present data. EDA allows a much greater appreciation of the features in the data than summary statistics or statistical significance levels. Outliers and obvious errors in the data can also be detected through EDA. A well-conducted EDA may eliminate the need for a formal statistical analysis. At the very least, the user should view a time series plot of the data (with a trend line fitted to the data) before using TREND.