This document describes the use of the functions implemented in TimeSeries.OBeu package in OpenCPU environment, after installing OpenCPU and TimeSeries.OBeu package on your OpenCPU server address(/ocpu/test).
Go to: yourserver/ocpu/test
Type to the endpoint:
../library/ {name of the library} /R/ {function}
If you want to see the function parameters you should:
Get
To run a function you should:
Post
Then you can click on Ajax Request.
In this example we will use ts.analysis
function that returns in a single call a json string or a list with the following components:
+-----------------------------+---------------------------+-------------------------------------------------------------+ | Component | Output | Description | +=============================+===========================+=============================================================+ | acf.parameters | - acf | - ACF values of the input time series | | | - acf.lag | - Lags at which the acf is estimated | | | - confidence.interval.up | - Upper limit of the confidence interval | | | - confidence.interval.low | - Lower limit of the confidence interval | +-----------------------------+---------------------------+-------------------------------------------------------------+ | pacf.parameters | - pacf | - PACF values of the input time series | | | - pacf.lag | - Lags at which the pacf is estimated | | | - confidence.interval.up | - Upper limit of the confidence interval | | | - confidence.interval.low | - Lower limit of the confidence interval | +-----------------------------+---------------------------+-------------------------------------------------------------+ | acf.residuals.parameters | - acf.res | - ACF values of the model residuals | | | - acf.res.lag | - Lags at which the acf is estimated of the model residuals | | | - confidence.interval.up | - Upper limit of the confidence interval | | | - confidence.interval.low | - Lower limit of the confidence interval | +-----------------------------+---------------------------+-------------------------------------------------------------+ | pacf.residuals.parameters | - pacf.res | - Pacf values of the model residuals | | | - pacf.res.lag | - Lags at which the pacf is estimated of the model residuals| | | - confidence.interval.up | - Upper limit of confidence interval | | | - confidence.interval.low | - Lower limit of confidence interval | +-----------------------------+---------------------------+-------------------------------------------------------------+ | stl.plot | - trend | - Trend component | | | - trend.ci.up | - Up limit for trend component | | | - trend.ci.low | - Low limit for trend component | | | - seasonal | - Seasonal component | | | - remainder | - Remainder component | | | - time | - Time of the series was sampled | +-----------------------------+---------------------------+-------------------------------------------------------------+ | stl.general | - stl.degree | - Degree of fit | | | - degfr | - Effective degrees of freedom | | | - degfr.fitted | - Fitted degrees of freedom | | | - fitted | - Model's fitted values | +-----------------------------+---------------------------+-------------------------------------------------------------+ | residuals | - residuals | - Residuals of the model | +-----------------------------+---------------------------+-------------------------------------------------------------+ | compare | - arima.order | - Arima order | | | - arima.coef | - AR, MA and regression coefficients | | | - arima.coef.se | - Standard error of the coefficients | | | - covariance.coef | - Variance of the coefficients | | | - resid.variance | - Residuals variance | | | - not.used.obs | - Number of not used observations | | | - used.obs | - Used observations | | | - loglik | - Maximized log-likelihood, | | | - aic | - AIC value | | | - bic | - BIC value | | | - gcv | - Generalized cross-validation statistic | | | - aicc | - Second-order AIC | +-----------------------------+---------------------------+-------------------------------------------------------------+ | forecasts | - ts.model | - A string indicating the arima orders | | | - data_year | - Time of time series data | | | - data | - Time series values | | | - predict_time | - Time of the predicted values | | | - predict_values | - Predicted values | | | - up80 | - Upper 80% confidence limit | | | - low80 | - Lower 80% confidence limit | | | - up95 | - Upper 95% confidence limit | | | - low95 | - Lower 95% confidence limit | +-----------------------------+---------------------------+-------------------------------------------------------------+
Table: ts.analysis
components
Go to: yourserver/ocpu/test
Copy and paste the following function to the endpoint
../library/TimeSeries.OBeu/R/ts.analysis # library/ {name of the library} /R/ {function}
Post
Click add parameters every time you want to add a new parameters and values.
Define the input data:
tsdata
Athens_executed_ts
Define the prediction steps parameter:
prediction.steps
2
You add likewise x.order parameter to fit a specific arima order, see TimeSeries.OBeu reference manual for further details.
copy the /ocpu/tmp/{this_id_number}/R/.val (second on the right panel)
finally, paste yourserver/ocpu/tmp/{this_id_number}/R/.val
on a new tab.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.