Weather Forecasting
It sounds pretty ordinary, but predicting the weather is one of the most difficult tasks that consider many factors.
The simplest models we studied earlier are often used in a more modified form. For example, these are models such as Box-Jenkins and Holt-Winters seasonal autoregressive integrated moving average, the autoregressive integrated moving average with external regressors in the form of Fourier terms.
When forecasting the weather, in addition to temperature, you can take into account the environmental zone, latitude, and longitude parameters.
The main characteristic of such data is seasonality; in accordance with this - you can choose the most suitable model.
The effectiveness of weather forecasting also depends on the time frame you choose. At the moment, in short-term predictions, vector autoregression models are more effective than, for example, SARIMA.
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It sounds pretty ordinary, but predicting the weather is one of the most difficult tasks that consider many factors.
The simplest models we studied earlier are often used in a more modified form. For example, these are models such as Box-Jenkins and Holt-Winters seasonal autoregressive integrated moving average, the autoregressive integrated moving average with external regressors in the form of Fourier terms.
When forecasting the weather, in addition to temperature, you can take into account the environmental zone, latitude, and longitude parameters.
The main characteristic of such data is seasonality; in accordance with this - you can choose the most suitable model.
The effectiveness of weather forecasting also depends on the time frame you choose. At the moment, in short-term predictions, vector autoregression models are more effective than, for example, SARIMA.
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