Scalecast: Machine Learning & Deep Learning - Time Series Data Handling
If you are looking for a course that provides solid foundations for creating machine learning and deep learning models that handle time series data, then the Scalecast course is definitely for you. Scalecast is an interface that allows uniform modeling, reporting, and data visualization using a diverse set of libraries, including Scikit-learn, Statsmodels, and TensorFlow. This course is perfect for those who want to learn how to create models for time series forecasting, especially with ARIMA and LSTM techniques.
What is Scalecast?
Scalecast is an interface that allows uniform modeling, reporting, and data visualization using a diverse set of libraries, including Scikit-learn, Statsmodels, and TensorFlow. It makes the creation of machine learning and deep learning models for handling time series data easy and painless, thanks to its powerful features.
The interface is designed to take a lot of the headache out of the implementation of time series forecasts. It employs TensorFlow under-the-hood, which gives it the ability to capture patterns in sequential data with ease. Scalecast offers a whole range of features, including lag, trend, and seasonality selection, hyperparameter tuning using grid search and time series, transformations, Scikit models, ARIMA, LSTM, and multivariate models, to name a few.
Advantages of Time Series Forecasting
One of the most significant benefits of time series forecasting is the ability to make predictions based on historical observations, giving organizations a competitive advantage. For instance, an organization that can forecast the sales quantities of its products better can optimize its inventory levels, resulting in increased liquidity of its cash reserves and a decrease in working capital. By reducing the backlog of orders, an organization can also improve customer satisfaction.
In machine learning, there are specific methods and techniques well suited for predicting the value of a dependent variable according to time. ARIMA is one of the essential techniques for time series forecasting. It is suited for univariate data, with each point in the series affected by its past values. It considers the underlying data to be stationery, with a constant mean and variance.
LSTM is a kind of recurrent neural network (RNN) that is optimized for handling sequential data. LSTM has an optimized architecture that makes it easy to capture the pattern in sequential data. The benefits of this type of network are that it can learn and remember over long sequences and does not rely on pre-specified window-lagged observations as input. The Scalecast library features a TensorFlow LSTM that makes it easy for learners to handle time series forecasting tasks.
Course Features
There are several features in the Scalecast course that make it stand out from the rest. The following are some of the best features that you will enjoy when you enroll in this course:
Lag, Trend, and Seasonality Selection
One of the most important aspects of analyzing time series data is selecting the appropriate values of lag, trend, and seasonality. Scalecast makes it easy to select the correct values for lag, trend, and seasonality and ensures their proper implementation in modeling the data.
Transformations
Transformations are vital when handling time series data. Scalecast offers a range of effective transformation methods that can be used to improve the quality and accuracy of the data, such as box-cox, log, and square root.
Scikit Models
Scikit-learn is a popular machine learning library that contains several algorithms for classification, regression, and clustering problems. With Scalecast, you can use Scikit models to create robust models quickly and easily.
ARIMA
ARIMA (AutoRegressive Integrated Moving Average) is a statistical method of analyzing and forecasting time series data. ARIMA models capture information about the relationship between the past and future values of the series. ARIMA is particularly well suited to time series data that is not stationary and has a non-constant mean and variance.
LSTM
LSTM (Long Short-Term Memory) is a type of recursive neural network that can learn and remember over long sequences. The Scalecast library has a TensorFlow LSTM that can make it easy for learners to manage and forecast time series data.
Reviews and Ratings
The Scalecast course has received excellent reviews and ratings from learners worldwide. To date, there are three reviews, with a rating aggregate of 4.88199. These reviews highlight the effectiveness of the course in developing machine learning and deep learning models for handling time series data.
Assignment
One of the most effective ways to learn is through practice. The Scalecast course includes an assignment that helps learners to practice what they have learned. The assignment is designed to test your understanding of the topics covered in the course and ensure that you can apply them in real-world scenarios.
Wrapping Up
With Scalecast, you will learn how to create machine learning and deep learning models for handling time series data. The course offers a range of effective features, including lag, trend, and seasonality selection, transformations, Scikit models, ARIMA, LSTM, and multivariate models, among others. The course has an assignment that allows learners to put their understanding into practice and refine their skills.
If you are looking for a course that will provide a solid foundation for creating effective machine and deep learning models for handling time series data, Scalecast is definitely the course for you.
