Note that due to the number of images, it could take a few seconds to load the first time.Īccess to the documentation: The model's theoretical description is also included in the toolbox. Click on DOCX report to save it in the MS Word 2017 format. The toolbox collects all results data of the model through tables and plots in this large sheet. Read and save the report: Click on Menu Reports>Complete report. Each graphic is can be zoomed-in and saved independently.Įxplore the dataset: residuals and auto-correlation: Click on Menu View>Residuals to see the residuals of the fitted model (after and before the change-point)Īnd their autocorrelation function to assess whether certain model assumptions have not been met. If you are wondering about the log-likelihood of the model, scroll down to see the plot. Details about the slope, intercept, and correlations estimates are also generated.Įxplore the dataset: fitted values and log-likelihood: Click on Menu View>Estimated model to see the time series differences between the fitted mean function with the raw data. Relevant results: Extending the data summary, in this table, you will see the difference between the estimated and your theoretical change-point proposal. Once you are sure of the intervals, press the Model Dataset button to start processing.ĭata summary: You will see a table in the top area with a brief summary of the processing results: the difference in the slope, levels, and correlations that appears in your data. Remember that longer intervals will require more time to process. You can use the bars to select the range. Sometimes, we will need a specific interval to analyze: use the bars to choose the start and end dates of the processing.Ĭhoose the potential change-point's interval: The statistical model requires an interval of theoretical (or potential) change-points. The input files should have three columns: the institute name, the date, and the registered value.Ĭhoose the date range: The toolbox will start showing a plot of the time series recorded in the dataset. Load the dataset: RITS can process CSV tables.We are providing binaries for Windows 圆4, macOS, and GNU/Linux (in an AppImage format). The easiest way to use RITS is to download the latest release. Example: COVID partial lockdown effect in Germany (November 2, 2020).If you are already using this software, consider to cite us. The user interface has been developed in JavaScript language programming, while the statistical model has been independently implemented in Nim language programming to ensure improved memory and speed efficiency. RITS provides a flexible, seamless interface for the analysis of time series data with the aim of improving the focus on the interpretation of the outcome while reducing the complexity of the use of statistical models. (as described in ) and RITS takes this difference into account. Note that the time of intervention and the true change-point in the system could not be the same Trend, or correlation structures that occur before and after a change-point. RITS analyzes interrupted time series (ITS) and estimates an intervention policy's effect through changes in level, The Robust Multiple Interrupted Time Series model and the Supremum Wald Test to evaluate the presence and impact during a change-point caused by a change in a policy. RITS implements the Robust Interrupted Time Series model , RITS is a stand-alone, cross-platform, and user-friendly application to assess the impact of complex policy interventions. Robust Interrupted Time Series Toolbox (RITS)
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