## Multivariate Time Series Forecasting with Neural Networks

In previous articles, we’ve introduced how to use XGBoost to forecast future time series data points. We did that with univariate data and also added additional or exogenous variables. However, one limitation is that XGBoost models are not capable of handling multivariate data. Before moving forward, let’s clarify what each Read more…

## Step-by-Step Guide to Time Series Forecasting with Vanilla RNN

In the last article, we introduced the theory behind Recurrent Neural Networks. This time we will use a simple example to illustrate the process of training a vanilla or basic RNNs to forecast time series data. We will import the basic libraries that we use in every single Data Science Read more…

## Theoretical Introduction to Recurrent Neural Networks

Are you interested in mastering Time Series forecasting or natural language processing? Then you should learn about Recurrent Neural Networks. Recurrent Neural Networks or RNNs are a specialized form of neural network architecture engineered for sequence-based tasks. Unlike traditional feed-forward neural networks, which treat each input as independent, RNNs excel Read more…

## XGBoost to forecast univariate Time Series data with exogenous variables

In the last article, we learned how to train a Machine Learning model like Linear Regression or XGBoost to forecast Time Series data. We had to reframe the dataframe as a supervised learning problem. You can read about this process here. To explain the process we used Forex data, specifically Read more…

## XGBoost to forecast univariate Time Series data

In a previous article, we talked about the Advanced Time Series models. We mentioned that these are models based on Machine Learning. Some of the most common ones are based on Neural Networks, these are: These are great examples, but before moving to explain them, we should have a look Read more…

## Advanced Time Series Forecasting Methods

So far we have been talking about classical approaches when forecasting time series data. However, it is essential to explore alternative techniques that involve advanced methodologies such as machine learning and deep learning. There are mixed views regarding the accuracy of these last techniques. Some say that these advanced techniques Read more…

## Can ChatGPT forecast if the price of Bitcoin will increase or decrease tomorrow?

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…

## Performance Metrics for Time Series Forecasting

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…

## Step-by-Step Guide to Multivariate Time Series Forecasting with VAR Models

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…

## Error Metrics for Time Series Forecasting

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…