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| en:iot-reloaded:data_preparation_for_data_analysis [2024/12/10 21:30] – pczekalski | en:iot-reloaded:data_preparation_for_data_analysis [2025/05/17 09:06] (current) – [Time series modelling] agrisnik | ||
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| * In the case of M = 10, the overall shape of the time series is preserved while noise is removed. | * In the case of M = 10, the overall shape of the time series is preserved while noise is removed. | ||
| * In the case of M = 100, the time series shape is transformed into a new function, which does not represent the main feature of the original measurements. For instance, the rises are replaced by falls and vice versa, while the data spike melts with the coming rise and forms one more significant rise of the signal. So, the result annihilates the initial features of the signal. | * In the case of M = 100, the time series shape is transformed into a new function, which does not represent the main feature of the original measurements. For instance, the rises are replaced by falls and vice versa, while the data spike melts with the coming rise and forms one more significant rise of the signal. So, the result annihilates the initial features of the signal. | ||
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| - | <figure Moving_average> | ||
| - | {{ {{ : |: | ||
| - | < | ||
| - | </ | ||
| === Exponential moving average === | === Exponential moving average === | ||
