Learn what regression analysis is and how to implement it in Python in this definitive step-by-step mini-course. If you’re interested, you can follow along with the video and the Linear Regression in Python Juypter Notebook. What Is Regression Analysis? Regression analysis …
Peter Naur first termed Data Science in the year 1974. But, the journey had begun in ancient times. Here’s a chronicle about the history of data science, tracing its path from the formative collection methods to the most advanced forms …
Artificial Intelligence is an indispensable part of the finance world due to its ability to understand complex mathematical relationships and its automation ability. With data and compute power growing exponentially, financial service providers turn to AI for cost-effective solutions. A …
Autoregression, or an autoregressive model, is a type of predictive modeling that uses linear regression on past values to predict the next value in a time series. You’ll learn what autoregression is and how to forecast the price of Bitcoin …
To analyze ARIMA results, you need to determine if the model meets the assumptions using Jlung-Box chi-square statistics and autocorrelation of residuals; understand if each term is significant using p-values, and recognize if your model fits well using mean-squared error. …
The following are the most important historical and current Artificial Intelligence statistics for those researching the markets or industry. Key Statistics Artificial intelligence plays a crucial role in optimizing and transforming businesses worldwide. This disruptive technology has recently gained a …
You can make a time series stationary using adjustments and transformations. Adjustments such as removing inflation simplify the historical data making the series more consistent. Transforms like logarithms can stabilize the variance, while differencing transforms stabilize the mean from trend …
You can use visual inspection, global vs. local analysis, and statistics to analyze stationarity. The Augmented Dickey-Fuller (ADF) test is the most commonly used parametric test, and the Zivot-Andrews test is better than the ADF at detecting stationarity through structural …
Stationarity means that a process’s statistical properties that create a time series are constant over time. This statistical consistency makes distributions predictable enabling forecasting, and is an assumption of many time series forecasting models. What Is Stationarity? In mathematics and …
Year-over-year (YoY) is a method of analyzing data between two comparable periods on an annualized basis. Understanding how to calculate and what YoY values are can help you interpret financial and economic data more effectively. But it’s not enough to …