Time Series Analysis With Generalized Additive Models

These correlations between past and present values demonstrate temporal dependence, which forms the basis of a popular time series analysis technique called ARIMA (Autoregressive Integrated Moving Average). Long short-term memory (LSTM) networks are a type of neural networks that builds models based on temporal dependence. Therefore, google search trends for persimmons could well be modeled by adding a seasonal trend to an increasing growth trend, in what's called a generalized additive model (GAM). The principle behind GAMs is similar to that of regression, except that instead of summing effects of individual predictors, GAMs are a sum of smooth functions.