In this post, you will learn how to easily forecast intermittent time series data using the StatsForecast library in Python. Intermittent time series data is unique in the world of forecasting because it often includes missing data, irregular values, or a large number of zeros. They usually start appearing when you go down in the hierarchy levels of a time series. Traditional forecasting methods can struggle with these types of data, but after reading this post, you’ll have all the tools you need to tackle even the most complex intermittent time series....

In this article you will learn an easy, fast, step-by-step way to use Convolutional Neural Networks for multiple time series forecasting in Python. We will use the NeuralForecast library which implements the Temporal Convolutional Network (TCN) architecture. Temporal Convolutional Network (TCN) This architecture is a variant of the Convolutional Neural Network (CNN) architecture that is specially designed for time series forecasting. It was first presented as WaveNet. Source: WaveNet: A Generative Model for Raw Audio...

What Is Naive Forecasting? Whenever you start a time series forecasting project, you should start with a naive model. A naive model is a very simple rule that you use to generate predictions for the future. It’s easy to implement and it gives you a baseline to compare your more complex models against. Here you will learn how to use the StatsForecast library, which provides the most popular naive models for time series forecasting in Python....

ARIMA is one of the most popular univariate statistical models used for time series forecasting. Here you will learn how to use the StatsForecast library, which provides a fast, scalable and easy-to-use interface for us to train ARIMA models in Python. To understand ARIMA, let’s take an example of sales forecasting. Suppose a retail store has historical sales data for the past 12 months. To make a sales forecast for the next 3 months, we can fit an ARIMA model to this data....

You’ve probably heard about LSTMs, and might be curious about how they can help you with multiple time series forecasting. As machine learning practitioners, we come across various forecasting tasks, and choosing the right model can sometimes be a challenge. LSTMs have gained attention for their ability to handle long-term dependencies in sequential data, making them a promising choice for time series problems. By the end of this tutorial, you’ll have a deeper understanding of LSTMs and be better prepared to use them effectively for multiple time series forecasting projects....

Forecasting time series is a very common task in the daily life of a data scientist, which is surprisingly little covered in beginner machine learning courses. It can be predicting future demand for a product, city traffic or even the weather. With accurate time series forecasts, companies can adjust their production strategies, inventory management, resource allocation and other key decisions, leading to significant cost reduction and increased efficiency. Furthermore, forecasts also allow companies to be more proactive rather than reactive, anticipating market trends and adjusting their strategies accordingly....

If you are using XGBoost with decision trees as your base model, you don’t need to worry about scaling or normalizing your features. Decision trees are not sensitive to the scale of the features. In practice, I have seen very minor differences in score by scaling[features for decision trees, but these are due to numerical computing implementations and not significant in practice. If you are using XGBoost with linear models as base models, it is a good idea to scale or normalize the features....

One thing you should be aware of when using marketing mix models is something called multicollinearity. This happens when two or more input variables in your model are highly correlated, making it tough to interpret the results. I like to play around with online ads, so I decided to give marketing mix models another try to learn more about them. I don’t actually have anything to sell, but I thought it would be a fun exercise to create an Instagram ad campaign to try and get more followers....

What Is Adstock in Marketing Mix Modeling? Adstock in marketing mix modeling is a way to take into account that the impact of an advertisement on consumer behavior may not be immediate, but rather may build over time. In other words, we need to adjust our model to the fact that changes in how people feel about a product or brand after seeing an ad can last even after the ad is no longer being shown....

I got very excited about a new technique in natural language processing (NLP) called Contrastive Search. It is a powerful tool that improves the quality of text generated by smaller language models. In this blog post, I will compare the outputs of contrastive search in a few open-source models with GPT-3. Finally, I will talk about the implications of this technique and how it could be used in the future. So, let’s get started!...