Applied Forecasting in Python | Air Quality Dataset | ARMA Model | Temperature Prediction
Python is a powerful programming language that is widely used for data analysis and scientific computing. It has a large ecosystem of libraries and packages that provide a wide range of forecasting algorithms and tools.
In this example, we will be using the Air Quality dataset, which contains information about the temperature, humidity, and other factors that affect the air quality. We will use a specific type of forecasting model called an ARMA (Auto-Regressive Moving Average) model to predict the temperature.
An ARMA model is a type of time series forecasting model that combines the strengths of two other models: the Auto-Regressive (AR) model and the Moving Average (MA) model. The AR model looks at the past values of the series to predict future values while the MA model looks at the error between the predicted values and the actual values.
First, we will preprocess the Air Quality dataset and split it into training and testing sets. Next, we will use the ARMA model to train on the training data. We will then use the model to predict the temperature for the testing data.
In summary, Applied Forecasting in Python using Air Quality dataset, ARMA model and Temperature prediction is a powerful combination of a programming language, dataset and techniques that can be used to predict future values of temperature. ARMA model is a time series forecasting model that combines the strengths of two other models, the Auto-Regressive (AR) and Moving Average (MA) models, and it is used to predict the temperature based on past values and error.
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: QDA in R.
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