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# How to do Time Series Forecasting in R – Neural Network model using lynx dataset

Neural Network (NN) is a method for time series forecasting that is particularly well-suited for data that have complex patterns and non-linear relationships. Neural Networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.

One of the datasets that can be used for Neural Network model is the lynx dataset which has historical data of the number of lynx trappings in Canada between 1821 and 1934. The goal of using Neural Network model on the lynx dataset is to predict the number of lynx trappings in future years, by learning the underlying patterns in the data.

The process of building a Neural Network 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. Neural Network is a complex model that can learn complex patterns and non-linear relationships in the data.
- Training the model. This includes adjusting the parameters of the model, such as the number of layers and neurons, 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, Neural Network model is a powerful method that can learn complex patterns and non-linear relationships in 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, Neural Network model is a powerful technique for time series forecasting and it can be applied to a wide range of datasets like lynx that have complex patterns and non-linear relationships. By learning the underlying patterns in the data, Neural Network 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 Time Series Forecasting in R – Neural Network model using lynx dataset.

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## How to do Time Series Forecasting in R – Neural Network model using lynx dataset

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.

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