Below, we begin a series of examples discussing the creation of time series style data in **fabricatr**. This document assumes you are familiar with the basics of building and importing data with **fabricatr**.

### Single unit fixed time trend data

The simplest possible example involves a single unit with specified, time-dependent data, with a linear trend. In this example we generate a geographic location that has a fixed linear time trend in GDP growth.

```
panel_unit <- fabricate(
N = 20,
ts_year = 0:19,
gdp_measure = 20 + 0.3 * ts_year + rnorm(N, sd=0.3)
)
```

First, we begin by creating tracking progress on the time trend, here `ts_year`

, which begins at 0 and increases by one across observations. Next, we create a variable that depends on the current value of `ts_year`

; here the GDP measure for our unit begins at 20 (log units) and increases by one third of a log unit each year. We also specify a stochastic error term.

## Multiple units with time trends

A more complex example might involve several geographic units, each of which has a separate growth value. Here we can use **fabricatr**’s support for multi-level, hierarchical data to elaborate:

```
panel_units <- fabricate(
countries = add_level(
N = 5,
base_gdp = runif(N, 15, 22),
growth_units = runif(N, 0.2, 0.8),
growth_error = runif(N, 0.1, 0.5)
),
years = add_level(
N = 5,
ts_year = 0:4,
gdp_measure = base_gdp + (ts_year * growth_units) + rnorm(N, sd=growth_error)
)
)
```

Here, each country-year inherits the parameters of the country: a base GDP, an annual growth rate (which is constant in this model), and an error parameter. The resulting data is 25 rows; 5 years for each of 5 countries.

### Multiple units with fixed global time trends

Note that it would also be possible to include a fixed global trend in this example by including it as part of the variable specification:

```
global_trend <- 0.1
global_trend_example <- fabricate(
countries = add_level(
N = 5,
base_gdp = runif(N, 15, 22),
growth_units = runif(N, 0.2, 0.8),
growth_error = runif(N, 0.1, 0.5)
),
years = add_level(
N = 5,
ts_year = 0:4,
gdp_measure = base_gdp +
(ts_year * global_trend) + (ts_year * growth_units) +
rnorm(N, sd=growth_error)
)
)
```

## Multiple units with global yearly shocks

Even more complex designs may include non-trend global level shocks (for example, financial crises or booms that affect all countries). The traditional hierarchical data design may not fit here, because we want common country-level data and common year-level data, both combined to form country-year observations. This is a good example of data that can best be described as multiple non-nested levels. Users interested in implementing this should review our manual on cross-classified and panel data. The below example will use `cross_levels`

and non-nested level data.

```
panel_global_data <- fabricate(
years = add_level(
N = 5,
ts_year = 0:4,
year_shock = rnorm(N, 0, 0.3)
),
countries = add_level(
N = 5,
base_gdp = runif(N, 15, 22),
growth_units = runif(N, 0.2, 0.5),
growth_error = runif(N, 0.1, 0.5),
nest = FALSE
),
country_years = cross_levels(
by = join(years, countries),
gdp_measure = base_gdp + year_shock + (ts_year * growth_units) +
rnorm(N, sd=growth_error)
)
)
```

Notice that each variable is specified in the appropriate level; time series year indicators and yearly shocks are specified at the year level; country-specific time trend information and base GDP are specified at the country level; and the actual GDP measure, which is country-year, is specified at the country-year level.

## Seasonal or ARIMA Time Series

Although **fabricatr** does not have formal functionality for the creation of ARIMA time series, we recommend that interested users see our guide to using other data creation packages with **fabricatr**, which includes an example of using the **forecast** package to generate ARIMA data.

## What’s next?

You may also be interested in our online tutorial on structuring panel and cross-classified data..