Applied Data Science Coding | Forecasting in R | SARIMA model | Air Quality Dataset
Data science is a field that uses various techniques to extract insights and knowledge from data. One important aspect of data science is forecasting, which involves using historical data to predict future events. Forecasting is important in many industries such as finance, weather, and even air quality. By making predictions about the future, we can make more informed decisions and take appropriate actions.
R is a popular programming language for data science and statistics, and there are many libraries and tools available for forecasting in R. One such tool is the SARIMA model. SARIMA stands for “Seasonal AutoRegressive Integrated Moving Average.” This model is used to forecast time series data, which is data that is collected at regular intervals over time (e.g. hourly, daily, or monthly) and has a clear seasonal pattern.
The SARIMA model is a combination of four components: a seasonal autoregression (SAR) component, an autoregression (AR) component, an integration (I) component, and a moving average (MA) component. The seasonal autoregression component represents the relationship between the current value of the time series and its past values of the same season. The autoregression component represents the relationship between the current value of the time series and its past values. The integration component represents the difference between the current value and the past value. Finally, the moving average component represents the error term, which is the difference between the actual value and the predicted value.
The Air Quality dataset is a dataset that contains information on air pollution levels in a certain area. This dataset can be used to predict future air pollution levels in the area, which is important for public health and environmental protection. Air pollution can cause several health problems such as respiratory diseases, heart attack and even cancer. By forecasting air pollution levels, we can take appropriate actions to reduce the exposure to polluted air and protect public health.
In order to use the SARIMA model to forecast air pollution levels, we first need to clean and prepare the dataset. This may involve removing missing or incomplete data, and transforming the data into a format that can be used by the model. Next, we would need to use R libraries and tools such as
forecast to train the model using historical air pollution data, and use it to make predictions about future air pollution levels.
Overall, the SARIMA model is a powerful tool for forecasting time series data that has a clear seasonal pattern, and can be used in a variety of applications, such as air quality forecasting. By using data science techniques and tools like R, we can gain valuable insights and make predictions about the future air pollution levels. This information can be used to take appropriate actions to reduce the exposure to polluted air and protect public health. Additionally, the SARIMA model can also help to understand how the air pollution levels change over time and how they are affected by different seasons, this information can be used to implement policies and regulations to improve air quality.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Applied Data Science Coding | Forecasting in R | SARIMA model | Air Quality Dataset.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R: