# Recent content by Mukund

1. ### Simple Equation

s-sx-p-px=px s-p =px+px+sx (s-p) / (2p+s) = x
2. ### Cross correlation and t tests

I have two time series data from two machines arranged sequentially ( machine 1 output goes is machine 2 input) . I wanted to compare the mean utlisation of these machines and see which one is higher over a given period of time. The two time series data is cross correlated. . So my question is...
3. ### Time series - descriptive statistics

My task is to summarises the descriptive statistics of time series data ( mean, SD , standard error ). It is fairly starightforward for a stationary series. But How do we find out the mean, standard deviation, standard error of the non stationary time series data? I have been reading about the...
4. ### Matt Whiteny U test Vs T test

Thank you for your reply. So can we use a t-test to compare the means always irrespective of the normality rule?
5. ### Matt Whiteny U test Vs T test

Hello fellow statisticians, Problem definition: I need to test whether the difference between the "mean" utilization metrics of two machines is statistically significant. Given Data: Utilisation data is available for two machines for more than 100 days. Therefore, the mean, standard...
6. ### time series analysis

Thank you for the fast response. Super clear explanation. Now I understand that the behavior of the aggregated series A will be different from its constituents (B and C) even though A=B+C.
7. ### time series analysis

Thank you for your comment. The logic to calculate the optimum window size. First, we divide the data into training and testing. We try different window sizes 10, 20, 30 etc. E.g. Window size 10: t1,....,t10 for foreacst 1, t2,....,t11 for forecast 2 etc. window size 20: t1,...., t20 for...
8. ### time series analysis

Hello, Thanks for your response. I calculated the window size based on error metrics (I used MAPE metric calculated over the training data set and compared it across different window sizes). I agree that the dynamics of the aggregated measure typically behave better than its constituents. But...
9. ### time series analysis

There is an aggregated measure represented by a variable A, modeled as a time series from a process. There was a need forecast A and also to find out the historical amount of data of A that is the best reflector of future values of A (as there was a data storage capacity issue). Using a...
10. ### ARIMA

There is an aggregated measure represented by a variable A, modeled as a time series from a process. There was a need forecast A and also to find out the historical amount of data of A that is the best reflector of future values of A (as there was a data storage capacity issue). Using a...
11. ### Which is the better prediction model?

The aim is to predict the breakdown time of a machine as a percentage of scheduled hours for the next day. So my time series looks like this, Break_down_percentage = 7%, 8%, 10%, 6%, 12 % etc. There are 315 data points which can be used to test the different models. I used ets(), arima()...
12. ### updating ARIMA model

When should one use the different types of ARIMA model as mentioned below: Estimate the model order in the training data set and use the same order to forecast future values (updating the parameter estimates) Use a rolling window (e.g. 30 day )to make a new forecast by estimating model order...
13. ### Anova or t-test on squared errors

I have two forecasting models, moving average and single exponential smoothing. The values of Mean Absolute Percentage Error (MAPE) is 5.2%, 5.8%. Since the difference of MAPE between the models are very close, I am quite confused which model to choose. Can we perform a t-test or ANOVA on...
14. ### Models comparison

I have a time series data of 1000 points for each of the different machines. I tried different forecasting techniques to make a one step prediction. The goal is to find out one common predictive model that could work for all machines. The forecasting techniques used are, 5 period moving...
15. ### Linear regression vs logistic regression

I have a time series dataset. The, X (Independent variable) is time and is denoted as 1,2,3,4,5,6..1000.etc Y (Dependent variable ) is a percentage scale as 99%, 98.7%, 96%, 91% ...etc. This is a continuous data set. I have 1000 such data points. The first 700 data points used as training set...