## Date Manipulation in Python for Time Series II

This previous article introduced the importance of correctly handling dates when working with time series data. In Python, there are multiple use cases and tools that must be known. This article is a continuation of the previous one, and we will explore more advanced techniques and tools to manipulate dates Read more…

## Date Manipulation in Python for Time Series

A key component of time series data is times and dates, and Python offers robust tools for effective manipulation. This article will provide a basic exploration of the different tools you have available for those purposes such as indexing, frequency adjustments, parsing dates, and more. Essential Libraries Let’s import the Read more…

## Stationarity in Time Series and how to check it

In the context of time series analysis, a time series is said to be stationary if its statistical properties such as mean, variance, and autocorrelation, remain constant over time. This means that no matter at what point in time you observe the series, the properties are the same. There are Read more…

## Time Series Forecasting with STL

STL stands for “Seasonal and Trend decomposition using LOESS”. It is a versatile and robust method for decomposing time series. This method decomposes a time series into its three main components: Classical decomposition methods vs. STL Before carrying on with STL, let’s introduce traditional methods. Here you have an example Read more…

## Forecast the popularity of YouTube searches with SARIMA

Numerous countries across the globe gear up for Christmas celebrations, and what better way to celebrate it than with a festive Data Science project? Let’s forecast the popularity of the “All I Want for Christmas” search by Mariah Carey on YouTube in the upcoming weeks. We can get the data from Google Trends. Read more…

## 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…