Can you use lasso for a time series?
I know it is not uncommon to use LASSO for time series. But as LASSO is actually only linear regression or a GLM with a constraint, what about the assumption of independent observations these models have?
What are violations of independence in time series regression?
Violations of independence are also very serious in time series regression models: serial correlation in the residuals means that there is room for improvement in the model, and extreme serial correlation is often a symptom of a badly mis-specified model, as we saw in the auto sales example.
Do you know the violation of the independence assumption?
I know that when using time series data the assumption that the errors are independent cannot be satisfied. In my opinion, it does not have a positive autocorrelation because I cannot see a cyclic pattern.
Are there any assumptions in the lasso estimate?
For the LASSO estimate, we cannot really compute standard errors anyway, so any assumption of dependence between the observations is inconsequential. Thanks for contributing an answer to Cross Validated!
How is Lasso used to predict stock market?
The forecasting of stock price movement in general is considered to be a thought-provoking and essential task for financial time series’ exploration. In this paper, a Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model is proposed as a novel method to predict financial market behavior.
Which is better lasso or ridge linear regression?
LASSO method is able to produce sparse solutions and performs very well when the numbers of features are less as compared to the number of observations. Experiments were performed with Goldman Sachs Group Inc. stock to determine the efficiency of the model. The results indicate that the proposed model outperforms the ridge linear regression model.
Is there Lasso-based forecasting of financial time series?
LASSO-Based Forecasting of Financial Time Series on the Basis of News Headlines Adrian Waltenrath⇤ 1Introduction In this paper, I carry out an interdisciplinary approach which is rather new and has drawn increasing attention recently. Since, theoretically, news articles contain all