Time Series data in Machine Learning Algorithms [closed]

Closed. This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 5 years ago. Improve this question I am relatively new to machine learning and have recently had some exposure to Decision … Read more

Data transformations for non-normal data

I conducted an experiment using a factorial design and have to analyze some of the variables using a repeated measures in time, which I am analyzing in SAS. I have never used a repeated measures ANOVA before. I work with biological systems and unfortunately they rarely meet the assumptions needed to run statistical analysis and … Read more

Looking ahead at seasonality in time series modeling without overfitting

In forecasting the performance of many agents in a time series, there is a strong seasonality component, in addition to non-seasonal features for each agent. How can I capture the overall seasonal trends in my model building process without overfitting? Currently, I’m using a time-series cross validation approach to fitting a Random Forest Regressor or … Read more

Predicting next event time

Problem definition: Predict user’s next event date, based on previous event occurrences. The aim is to inter-corporate time dependent and time independent features. Data: +10 year transactional data generated by millions of users. 80% of users have less than 3 events. Prediction: Next event date I’ve gone through many similar questions, the most related ones … Read more

Intuition/interpretation for the value of the spectral density calculated at the zero frequency

Is there any easy way to think about the value of the spectral density at the zero frequency? How do people interpret it usually? I have been studying some time series on my own and encountered a CLT for dependent data (covariance stationary and ergodic processes). The sufficient conditions of that CLT fail if that … Read more

Exercise on cointegrated processes

I have this time series problem that I cannot solve: Considering two I(1) independent stochastic processes zt and xt and a stationary stochastic process wt, I have the following DGP: yt=xt+zt+wt. Suppose that I misspecify the DGP and regress yt only on xt and zt. Will I be able to find cointegration between xt and … Read more

ACF of AR(p) models

Is there any other method to compute the sample autocorrelation function (ACF) of AR(p), the pth autoregressive model except the Yule-Walker equations? I want to determine if ACF is 0 except at lags 1,5,6,7 for the model: Xt =Zt+ ϕ1Xt−1+ ϕ5Xt−5 + ϕ6Xt−6 + ϕ7Xt−7 And if the ACF is 0 except at lags 1,5,6,7 … Read more

Pattern Recognition Time Series via FFT

I came across this interesting article where the author used FFT to discover some patterns in a time series. I am new to this kind of analysis and have maybe some basic questions about it. How do you compute the Frequency when getting the FFT? I used the fft function in MATLAB with some data … Read more

TimeSeriesSplit for multiple features in training set [closed]

Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it’s on-topic for Cross Validated. Closed 4 years ago. Improve this question I am trying to use Time-Series Split to establish a training and testing dataset and encountered the problem that I can not incorporate … Read more

Look at the time series plot, how to tell if the data is “random” and nonnormal or not?

I tried to solve this exercise in the book of “Time Series Analysis with Application in R”: Simulate a completely random process of length 48 with independent, chi-square distributed values, each with 2 degrees of freedom. Display the time series plot. Does it look “random” and nonnormal? Repeat this exercise several times with a new … Read more