To continue the first post of a series on forecasting, let’s discuss standard methods used for predicting time series. The two firsts methods can be used as benchmark for comparing with more advances models:
Mean value: the mean of the time series used for training is used as the forecast for all values in the test time series.
To continue the first post of a series on forecasting, let’s discuss standard methods used for predicting time series. The two firsts methods can be used as benchmark for comparing with more advances models:
Mean value: the mean of the time series used for training is used as the forecast for all values in the test time series.
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Last value: the last value of the time series is used as the forecast for the next one.
Simple Moving Average (SMA): the last m values are averaged to predict the next one.
Weighted Moving Average (WMA): the last m values are averaged, with a more important weight for recent values, to predict the next one.
Other approaches exist such as Exponential Moving Average (EMA), ARIMA, Neural Network (NN) and Support Vector Regression (SVR). Do you use other techniques for forecasting? Which one is better according to your experience?
