# Time Series Forecasting in R – Seasonal Random Walk model using lynx dataset

Seasonal Random Walk (SRW) is a method for time series forecasting that is particularly well-suited for data that exhibit a strong seasonality pattern, such as regular fluctuations that occur at specific time intervals. The SRW model assumes that the future value of a time series is likely to be similar to the current value, plus or minus any seasonal component.

One of the datasets that can be used for SRW 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 SRW model on the lynx dataset is to predict the number of lynx trappings in future years.

The process of building an SRW 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:** SRW model is a simple model that assumes that the future value of a time series is likely to be similar to the current value, plus or minus any seasonal component.

**Training the model:** This includes estimating the parameters of the model, such as the seasonal component, 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, SRW model is a simple method that can be useful when working with data that exhibits a strong seasonality pattern. 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, SRW model is a simple technique for time series forecasting and it can be applied to a wide range of datasets like lynx that have a strong seasonality pattern. By assuming that the future value of a time series is likely to be similar to the current value, plus or minus any seasonal component, SRW model can provide 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: Time Series Forecasting in R – Seasonal Random Walk model using lynx dataset.

## Time Series Forecasting in R – Seasonal Random Walk 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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