A Quick Guide to Facebook Prophet for Time Series Analysis
Facebook prophet is an open source forecasting tool available for Python and R. Prophet makes it easier for experts and non-experts to make high quality forecasts at scale by simplifying the forecasting process and providing an improved predicting ability. Prophet uses an additive regression model which represents a time series with an overall trend as well as a combination of patterns at different scales, such as daily, weekly, or monthly.
Detrending Time Series Data
Before you can begin modeling on time series data, you need to make sure that the data you have is stationary. Stationarity is important because if the mean or variance of a time series fluctuates throughout time, a model describing the data will vary in accuracy at different time points due to the changing mean/variance. Since most time series data is not stationary, this is an important step when dealing with time series data.
Using Gridsearch to Optimize Model Performance
Hyperparameters are parameters that are not directly learnt within a model and must be set before the learning process begins. Gridsearch is used to find the optimal hyperparameters of a model to optimize model performance. Gridsearch will exhaustively consider all hyperparameter combinations within a grid of parameter values and return the optimal values for each hyperparameter.
Detecting Pneumonia in X-Ray Images Using Convolutional Neural Networks
Correctly diagnosing conditions and diseases of patients quickly and accurately allows for the timely treatment of patients as well as freeing up time for medical professionals that would have otherwise been spent diagnosing. If we can create a deep learning model that can accurately classify whether a patient has a condition or not, medical professionals will be able to use the models to better diagnose their patients.