How to do Damped Trend Linear Exponential Smoothing model using lynx dataset – Time Series Forecasting
Damped Trend Linear Exponential Smoothing (DT-LES) is a variation of the Linear Exponential Smoothing (LES) technique that is used to forecast future values of a time series. It is a more advanced method that is particularly well-suited for time series data that exhibit a trend that is likely to change in the future.
One of the datasets that can be used for DT-LES model is lynx dataset which has historical data of the number of lynx trappings in Canada between 1821 and 1934. The goal of using DT-LES model on lynx dataset is to predict the number of lynx trappings in future years, taking into account that trend of the data may change over time.
The process of building a DT-LES model typically involves the following steps:
Collecting and cleaning the data: This includes acquiring the lynx dataset and preparing it for analysis.
Decomposition of time series: This step is optional but it is useful to understand the trend, seasonality and residual components of the time series.
Choosing an appropriate model: DT-LES model is a linear model that considers the trend and seasonality of the time series, but it also includes a damping factor that allows the model to account for a change in trend over time.
Training the model: This includes estimating the parameters of the model, such as the smoothing coefficients and the damping factor, using the historical data.
Forecasting: This includes using the trained model to predict future values of the time series, such as the number of lynx trappings in future years.
Evaluation: This includes evaluating the model’s performance on a separate test dataset and comparing it to other models or to a baseline.
It is important to note that time series forecasting is a complex task and there are many factors that can affect the accuracy of the forecasts. Additionally, DT-LES model is a more advanced method that can better handle changes in the trend of the data. However, it’s important to use appropriate techniques and to keep in mind that the predictions made by the model are only as accurate as the data it is trained on.
Overall, DT-LES model is a powerful technique for time series forecasting and it can be applied to a wide range of datasets like lynx. By understanding the trend and seasonality of the time series data and taking into account the changes in the trend, the DT-LES model can provide more accurate predictions for future values. However, it’s important to use appropriate techniques and to keep in mind that the predictions made by the model are only as accurate as the data it is trained on.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: How to do Damped Trend Linear Exponential Smoothing model using lynx dataset – Time Series Forecasting.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R: