# Time Series Visualization and Forecasting from Australian Building and Construction Statistics
###### Abstract  
The Australian Bureau of Statistics (ABS) regularly releases statistical information, for the whole of Australia, for public access. Building- and construction-related statistics are important to reflect the status of this pillar industry of Australia and help researchers, practitioners, and investors with decision-making. Due to complex retrieval hierarchy of ABS's website and irregular update frequency, it is usually time-consuming to find relevant information. Moreover, browsing the raw data from ABS's webpages could not provide the insights to the future. In this work, we applied techniques from computer science to help users in the building and construction domain to better explore the ABS statistics and forecast the future trends. Specifically, we built an integrated Web application that could help collect, sort, and visualize the ABS statistics in a user-friendly and customized way. Our Web application is publicly accessible. We further injected our insights into the Web application, based on the existing data by providing online forecasting on user's interested information. To achieve this, we identified a series of related economic factors as features and adjusted a multi-variant, LSTM-based time series forecasting model by considering the most informative factors. We also compared our approach with the most widely used SARIMA-based forecasting model to show the effectiveness of the deep learning-based models. The forecast values are depicted at the end of the time series plots, selected by the users. A +
Footnote †: journal: Medical  
The application will be helpful for people who are frequently viewing the ABS website. Businessman, investors, builders, and renters can all be benefited from this application. Property investors can get an overview of the price trend from the past to present, as well as the hot zones in Australia, in order to decide which place may be a potential investment location. For builders, they can compare the building and construction cost in each area to plan their budget wisely. A business person could learn the latest information about the construction and housing market. In addition to allowing users explore the current data from ABS easily and efficiently, we believe forecasting the future will be an essential function to help the decision making. This is formed as a time series forecasting problem. Uni-variant models predict the future, based only on historical values in the time series. The seasonal auto-regressive integrated moving average (SARIMA) [1; 2; 3] model is one example that has been used to date, thanks to its good performance and ability to handle non-stationery time series. The SARIMA model is an extension of the ARIMA model, which has both non-seasonal and seasonal parts. The non-seasonal is the same as the ARIMA model. In recent years, deep learning-based models [4] have been seen to excel at most of the learning tasks. Long short-term memory (LSTM) [5] is a recurrent neural network model. It has strong ability to handle sequence data. Therefore, it is the most commonly adopted deep learning model for time series forecasting. Most of the existing works [5; 6; 7; 8; 9; 10] feed the LSTM-based models with historical time series, that is, using historical time series as features to forecast the future values over the time or choose highly correlated feature for learning. In our work, we aim to explore building- and construction-related economic factors as features in our forecasting task to inject more relevant information into the learning process. In this work, we address the aforementioned two main tasks, i.e., Web-based application, plus time series forecasting.