Group 8 Report
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Contents
Abstract
During our time-series project experiences, we spent considerable efforts in experimenting with many variations of parameter configurations to analyse time-series data. This difficulty stems from the lack of tools that can help calculate the optimized time-series parameters automatically during model training.
To tackle this challenge, we created an easy-to-use time-series exploration system that is assessible even to the uninitiated analyst. The system is able to decompose the time series data to its constituent parts, namely Seasonality, Trend and Random (Noise). It can generate several forecasting models, i.e Exponential Smoothing, ARIMA, to predict future time periods using optimization techniques. The system also allows other forms of time series data to be displayed and their forecasts compared using the given forecasting methods, within certain formats.
The Consumer Price Index (CPI), with its short-term forecasts, is often used for tuning Governmental policies to steer inflation rates in countries like Singapore and for foreign investors to consider allocating potential investment funds into the country. Hence, we use Singapore’s CPI as our use case to understand the current state of Singapore’s living standards.
1. Motivation of the application
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2. Review and critic on past works
<JMP, Tableau, SAS EM>
3. Design framework
3.1 Interface
Our system's look-and-feel is designed in accordance to Shneiderman's mantra, his famous quote being: "Overview, zoom and filter, details on demand". This is apparent in the way we organize the steps and our UI elements.
Overview
From the first tab: "Upload Data File", we allow the user to upload a time-series csv file and they will be able to preview the kind of data that is going to be analyzed. There is also a table located at the side panel describing the metadata of the uploaded data. These features allow a general feeling of the data before any actual data analysis.
Zoom and filter
In the second tab, "Exploration", the left panel showcases a comprehensive set of filters that allow the user to narrow down the records in the dataset. Selection of different values would automatically result in the update of individual time-series charts on the main screen area, showing the zoomed in trend and seasonality data that was derived from the observation data.
Details on Demand
In the third tab, "Forecasting", the data has been focused and the user is now able to implement forecasting. Models with optimized parameters will be generated right from the start to provide convenience to the user and displayed in a sortable datatable. Forecast charts after model selection also extend and contract based on the number of models selected to provide some ease of visual comparisons. The forecasted time periods use a stark red line to denote its importance, along with two different shades of blue to denote the confidence intervals. Additional holdout data is also in a different shade of cyan, with this information present in the legend. Due to the possible small size of the charts when multiple have been selected, title bars and background gridlines have been provided to guide the user.
Our general system color scheme uses a calming shade of blue, with great areas of white and light grey. This color scheme is meant to reduce anxiety for data analysts trying to perform time-series data analysis. We also used consistent UI elements (from the Shinythemes package) throughout the pages to give users a sense of familiarity. UI controls are almost always placed on the left and visualizations on the right.
3.2 Functionality Design
What functions do we provide? Why group in such a way?
4. Demonstration
- Talk about CPI as Use Case
- Sample test cases
5. Discussion
What has the audience learned from your work? What new insights or practices has your system enabled? A full blown user study is not expected, but informal observations of use that help evaluate your system are encouraged.
6. Future Work
The current application is only able to process limited types of data. In the future, it shall cater for various data formats, such as weekly, quarterly and even data captured in miniscule time-scales: seconds or minutes.
We shall enhance it further to detect and show proper instructions or corresponding error messages when one tries to perform operations that exceed the system’s abilities.
7. Installation guide
including hardware configuration and software integrationn.
8. User Guide
Step-by-step guide on how to use the data visualisation functions designed.
