Univariate time series forecasting is the process of using a single time series data set to make predictions about future values of the series. This type of forecasting is typically used when there is only one variable or measure that is of interest, and the goal is to make predictions about how that variable will change over time. Time series forecasting involves analyzing past data to identify trends and patterns, and then using that information to make predictions about future values of the series. This can be done using a variety of techniques, such as regression analysis or machine learning algorithms.
Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in … Read more
Financial analysts have long been fascinated by the prospect of predicting the prices of financial assets. In recent years, there has been increasing interest in using machine learning and deep learning techniques to generate predictions, in addition to traditional methods such as technical and fundamental analysis. Python libraries like Keras and Scikit-Learn make it relatively … Read more
