Time Series in Python

The root mean square error (RMSE) is a metric that tells us how far apart our predicted values are from our observed values…

In time series analysis, a moving average is simply the average value of a certain number of previous periods. An exponential moving…

Autocorrelation measures the degree of similarity between a time series and a lagged version of itself over successive time intervals. It’s also sometimes…

A moving average is a technique that can be used to smooth out time series data to reduce the “noise” in the data and…

The mean squared error (MSE) is a common way to measure the prediction accuracy of a model. It is calculated as: MSE = (1/n) *…

The mean absolute percentage error (MAPE) is commonly used to measure the predictive accuracy of models. It is calculated as: MAPE = (1/n) * Σ(|actual…