How to capture trend in time-series data for forecasting using scikit-learn's LinearRegression()
Linear regression fits the data into a linear model basically a function Y = W*X with coefficients w = (w1, …, wp) with minimized residual sum of squares between the true values and its corresponding predicted values.
Obviously, time-series data, by nature, is not linear. In order to capture seasonality and cyclic patterns, I would suggest you to use polynomial function, at least with the power of n > 2. You can use more advance regression models such as support vector and random forest models.
But for sure, you can start from linear model. Then later, you can easily shift to other advance models after realizing the limitations of linear models.
I have read some literature about time series forecasting with ML. I get the concepts of

trend
seasonality
cyclic
noise

I would like to use scikit-learn's LinearRegression() as a start to make predictions. If I get it right, I can capture seasonality and cyclic with some feature engineering like day_of_week, month or seasons. I don't get it though, how to capture trend in the data. Is it lag features or a column calculating differences instead of totals?