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  1. M

    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.
  2. M

    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...
  3. M

    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...
  4. M

    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...
  5. M

    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...
  6. M

    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()...
  7. M

    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...
  8. M

    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...
  9. M

    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...
  10. M

    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...
  11. M

    Prediction Intervals

    I am in the process of calculating the prediction intervals on a time series data linear regression model The independent variable in my model is time measured as 1,2,3,4,...,40. I have a dependent variable which is a continuous variable. Now when calculating the prediction intervals using...
  12. M

    Regression Type Estimation

    Hello, Attached is a time series data that has a plot of the percentage of breakdowns on a scheduled hours across the days. The objective is to predict the value for the next day. I tried to use linear regression to fit the data, but got a p-value higher than 0.05 and R square value less than...