Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites.<\/p>\n
The EMC Data Science Global Hackathon dataset, or the \u2018Air Quality Prediction<\/em>\u2018 dataset for short, describes weather conditions at multiple sites and requires a prediction of air quality measurements over the subsequent three days.<\/p>\n An important first step when working with a new time series forecasting dataset is to develop a baseline in model performance by which the skill of all other more sophisticated strategies can be compared. Baseline forecasting strategies are simple and fast. They are referred to as \u2018naive\u2019 strategies because they assume very little or nothing about the specific forecasting problem.<\/p>\n In this tutorial, you will discover how to develop naive forecasting methods for the multistep multivariate air pollution time series forecasting problem.<\/p>\n After completing this tutorial, you will know:<\/p>\n Let\u2019s get started.<\/p>\n How to Develop Baseline Forecasts for Multi-Site Multivariate Air Pollution Time Series Forecasting This tutorial is divided into six parts; they are:<\/p>\n The Air Quality Prediction dataset describes weather conditions at multiple sites and requires a prediction of air quality measurements over the subsequent three days.<\/p>\n Specifically, weather observations such as temperature, pressure, wind speed, and wind direction are provided hourly for eight days for multiple sites. The objective is to predict air quality measurements for the next 3 days at multiple sites. The forecast lead times are not contiguous; instead, specific lead times must be forecast over the 72 hour forecast period. They are:<\/p>\n Further, the dataset is divided into disjoint but contiguous chunks of data, with eight days of data followed by three days that require a forecast.<\/p>\n Not all observations are available at all sites or chunks and not all output variables are available at all sites and chunks. There are large portions of missing data that must be addressed.<\/p>\n The dataset was used as the basis for a short duration machine learning competition<\/a> (or hackathon) on the Kaggle website in 2012.<\/p>\n Submissions for the competition were evaluated against the true observations that were withheld from participants and scored using Mean Absolute Error (MAE). Submissions required the value of -1,000,000 to be specified in those cases where a forecast was not possible due to missing data. In fact, a template of where to insert missing values was provided and required to be adopted for all submissions (what a pain).<\/p>\n A winning entrant achieved a MAE of 0.21058 on the withheld test set (private leaderboard<\/a>) using random forest on lagged observations. A writeup of this solution is available in the post:<\/p>\n\n
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Photo by DAVID HOLT<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
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Problem Description<\/h2>\n
+1, +2, +3, +4, +5, +10, +17, +24, +48, +72<\/pre>\n