Contents
How to analyze time series to predict binary outcome?
I want to analyze two series of variables in a span of time to predict a binary outcome. Then at 12:00 am i stop collecting this data and i see at 4:00 pm if it rains or not. With a big dataset i want to predict given the time seres data collected till 12:00 am if it will rain at 4:00 pm.
How to predict a binary outcome using Seres?
I want to analyze two series of variables in a span of time to predict a binary outcome. Then at 12:00 am i stop collecting this data and i see at 4:00 pm if it rains or not. With a big dataset i want to predict given the time seres data collected till 12:00 am if it will rain at 4:00 pm. How can i accomplish this?
How can I Predict parallel time series with multiple features?
Problem is that LSTM takes input as 3D i.e (n_sample, n_timesteps, n_features) and I can pass a single time series for a specific store (e.g. A) If I had a univariate mutiple time series I can reshape my input data as follows and pass it to LSTM. But I need to identify how can I predict parallel multivariate time series?
Which is the best model for binary classification?
There are many models that you can use for binary classification problems, such as logistic regressions, linear discriminant analysis, K-nearest-neighbours, trees, random forest, support vector machines, etc. You can review some of these methods in the book An Introduction to Statistical Learning from James, G. et al, specially chapter 4.
Is there a way to predict the outcome of the day?
I want to know if studying the two time series (the difference from the start or other parameters) is there a way to predict the outcome of the day
How to predict given the time Seres data collected?
With a big dataset i want to predict given the time seres data collected till 12:00 am if it will rain at 4:00 pm. How can i accomplish this? I was thinking about a k-nearest neighbors regression type analysis but i’m not sure how can i implement this.
Why do we use multivariate time series analysis?
There are lots of projects with univariate dataset, to make it a bit more complicated and closer to a real life problem, I chose a multivariate dataset. Multivariate time series analysis considers simultaneous multiple time series that deals with dependent data. (The dataset contains more than one time-dependent variable.)