TEN Project Proposal

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PROJECT DETAILS

PROJECT POSTER

PROJECT APPLICATION

REPORT

Problem & Motivation

Forecasting time series data requires technical knowledge and specialised software, making it difficult to do for most people. Even for those with the required resources, completing the entire forecasting process requires many complicated steps to be done manually, making it a tedious process. This problem is exacerbated when forecasting for different datasets is required and the entire process is repeated multiple times. Therefore we would like to create a web application for business and government agencies to be able to assist these organisations in predicting the future performance of their operations by simplify the process.

Approach

The application aims to provide users with the following capabilities:

1. To be able to allow users to select their own data for analysis
This will be achieved by dynamically generating the filters based on the columns in the dataset. Taking the Singapore Tourism data as an example, users will be able to filter by Country and Date.

2. To allow users to provide inputs for exploring and understanding their data through the visualisations
During the data discovery and exploration of the time series data, the exploration will be made as realistic as possible. For example, correlogram includes specifying of lags/periods, hence we will give the user the autonomy to explore the data by specifying their own input value.

3. To allow users to generate forecasts quickly and easily
This will be achieved by providing selections and inputs for the users to generate forecasts of different methods.

4. To allow users to compare the performance of the generated forecasting results
This will be achieved by providing various forecasting methods and the results of each forecasting methods such that users can estimate the accuracy of the models.

Related Works

The team has looked into some visualisations that was created on the Singapore’s tourism sectors, which gave us ideas on the factors or areas that the team can also looked into. The visualisations also provided us with an understanding of the tourism industry over the years and the different visualisation techniques.

Knoema

Knoema provides access to a large number of databases and visualisations tools for those databases to the public. From the visualisation of Singapore’s tourism from Knoema, the team has identified some factors which can be looked into such as the Government’s spending on tourism. However the team finds that the visualisations could be done better if it can present how the various factors are correlated, as currently the various factors are independent. Hence, the team will be taking this into consideration when designing our visualisations.

Public Tableau

The Tableau visualisation looks at the Singapore’s tourism industry from various aspects up to year 2013. Unlike the visualisations from Knoema, the Tableua visualisation further looks into the happenings in Singapore such as the opening of RWS which is closely related to the tourism industry and also the revenue and occupancy of the hotel industry which could be affected by the number of tourists. It also supported the observed trends with reasons which explains the drop or increase in tourist arrivals, which provided us with an overview of the tourism industry between 2009 to 2013. With this, our team has also identified areas which we can look into such as the hotel industry which is closely related to tourism.

ISSS608 Group 9

While researching, the team realised that Group 9 [1] of 2015/16 T1 of ISSS608 has also worked on Singapore’s tourism industry for their project. Thus, the team has looked into the visualisation techniques that was used and the factors and areas that they have considered for their project. The ISSS608 team has visualised the visitors’ data using a sunburst diagram which allows for further drilling down by continent, by country and by mode of transport which we find that it is an interesting way of visualising the data. Furthermore, the references provided has also provided us with more sources for data.

References

Related Works

1. Visualisation on Singapore’s Tourism Industry by Knoema
2. Visualisation on Singapore’s Tourism Industry by Public Tableau
3. Visualisation on Singapore’s Tourism Industry by Group 9 of 2015/16 T1 ISSS608 Visual Analytics and Applications [2]

Data sources

1. CEIC: Data on Visitor arrivals, Revenue and Expenditure
2. Singapore Tourism Board (STB): Data on Hotel statistics and Tourism sector performance

Key Challenges

ShinyR

In order to leverage on the various forecasting models that is available in R and its libraries, our group will be be using ShinyR to develop our web application. ShinyR also provides the platform to create a dashboard.However, our team forsee the learning of R to be a major challenge as none of us has build a web application with ShinyR, and based on intital research, the R syntax is also quite different.

Forecasting Model & Visualisations

As our group is working on providing a platform for forecasting, we needed to understand the various methods that are available and the visualisations that will be useful for the users to identify the hidden patterns in their data.

Milestones

Future Work

1. Comparing multiple forecasting methods & finding the best
2. Forecasting of stream data
3. Allowing wider range of data formats

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