Evaluation metrics, also known as performance measures or evaluative metrics, are quantitative measurements used to evaluate the performance and quality of a model or algorithm in solving a particular problem. It provides a standardized way to evaluate and compare different models and algorithms based on specific criteria. Error Metrics Error Read more…
This is a special article. We will try to get a model that could be able to predict whether the price of Bitcoin will increase or decrease the next day. However, we will use a different approach today, we will ask ChatGPT. We will pretend we don’t know anything (or Read more…
In a previous article, we introduced the so-called “Error Metrics“, which focus on measuring the accuracy and magnitude of errors in the forecasted values when compared to the actual values. They emphasize the magnitude of errors rather than the specific direction and provide insights into the overall performance and precision Read more…
In a previous article, we introduced Vector Auto-Regression (VAR), a statistical model designed for multivariate time series analysis and forecasting. VAR provides a robust solution by effectively capturing dynamic relationships between multiple variables over time. In this article, we will train a VAR model step-by-step. We will use the dataset Read more…
Evaluation metrics, also known as performance measures or evaluative metrics, are quantitative measurements used to evaluate the performance and quality of a model or algorithm in solving a particular problem. It provides a standardized way to evaluate and compare different models and algorithms based on specific criteria. In our case, Read more…
We have talked about ARIMA and SARIMA models previously, however, we have never shown a real case step by step. Let’s first recap, to make sure we know what an ARIMA model is. ARIMA model ARIMA (Auto-Regressive Integrated Moving Average) is a popular time series forecasting model that combines autoregressive Read more…
We talked about Vector Autorregression or VAR in a previous article. But, does it really make sense to use two different variables to get a forecast? The answer is no, not always at least. It will only be beneficial if there is some kind of relationship between them. Using unrelated Read more…
In the previous part of our Facebook Prophet series, we covered how to model the seasonality component. You should also recall the first part, in which we dealt with trend modelling. In this article, we’ll focus on how we can add exogenous variables to our model. They are also known Read more…
Ensemble Learning is a powerful method used in Machine Learning to improve model performance by combining multiple individual models. These individual models, also known as “base models” or “weak learners,” may have limitations such as high variance or high bias. There are two main types of ensemble models: There are Read more…
There are times when we need to forecast several variables at the same time. For these occasions, traditional methods such as ARIMA or Exponential Smoothing are not sufficient since they are univariate methods. Vector AutoRegression (VAR) is a statistical model for multivariate time series analysis and forecasting. It is used Read more…