Time Series Forecasting in R – Seasonal ARIMA model using lynx dataset

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Time Series Forecasting in R – Seasonal ARIMA model using lynx dataset

 

 

Seasonal ARIMA (SARIMA) is a method for time series forecasting that is particularly well-suited for data that exhibit both a trend and a seasonality pattern, such as regular fluctuations that occur at specific time intervals. The SARIMA model is an extension of the traditional ARIMA model, which stands for Auto-Regressive Integrated Moving Average, that includes an additional seasonal component.

One of the datasets that can be used for SARIMA 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 SARIMA model on the lynx dataset is to predict the number of lynx trappings in future years, taking into account both trend and seasonality patterns in the data.

The process of building a SARIMA 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: SARIMA model is a linear model that considers both the trend and seasonality of the time series.

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, SARIMA model is a more advanced method that can better handle both trend and seasonality patterns 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, SARIMA model is a powerful technique for time series forecasting and it can be applied to a wide range of datasets like lynx that have both trend and seasonality patterns. By considering both trend and seasonality of the time series, SARIMA 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: Time Series Forecasting in R – Seasonal ARIMA model using lynx dataset.

 

Time Series Forecasting in R – Seasonal ARIMA model using lynx dataset

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