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    How to statistically compare the trend similarity of two distinct time series?

    I have been studying time series and the time series I'm working with is non-stationary.
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    How to statistically compare the trend similarity of two distinct time series?

    The problem with slicing data is on the empirical methods implied which you don't control. Since I'm trying to look at a possible standard approach that will deal with the variations between data points, I would need something more intuitive. Nonetheless is a method that I might consider.
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    How to statistically compare the trend similarity of two distinct time series?

    As a biologist, I thought that the trend comparison between two distinct time series would be fairly simple. My first approach would be the application of a "statistical method" which would look at two data points (A and B; B and C; C and D...) at the correspondent time (0 and 1; 1 and 2; 2 and...
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    How to statistically compare the trend similarity of two distinct time series?

    Well, I will return to what brought me here to clarify. Again, I have the same data (NDVI) from different sensors (1 and 2). One of the sensors (1) is way more precise than the other(2) and for that reason, I want to validate the trend of the sensor (2) with (1). Remember that is not the numeric...
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    How to statistically compare the trend similarity of two distinct time series?

    Well, the comparison of univariate time series is what I have in my hands right now. I was surprised about the lack of information that is not available on this topic. I guess it follows your thoughts on complexity issues.
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    How to statistically compare the trend similarity of two distinct time series?

    I'm not sure if I understood the question. The distinct time series data come from 2 different sensors which measures the same thing (NDVI). However, the measures are different due to the different sensor sensitivities (both data are legit). A common latent variable does not exist in this case.
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    How to statistically compare the trend similarity of two distinct time series?

    Thank you so much for your help. It's been quite difficult to find solutions for this question. If you could tell me what is the package of ARDL tool built-in R recently I will be very grateful. About the ARIMA I will follow your advice and go with auto.arima in R. I might have some doubts in...
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    How to statistically compare the trend similarity of two distinct time series?

    Thank you so much for your answer! If I'm stationary, instead of a trend can I have a pattern? (: I don't know ARIMA but it has been one of the most spoken statistical methods to analyse the trends (so far in my research) and I will consider as an option as well the autoregressive distributed...
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    How to statistically compare the trend similarity of two distinct time series?

    Thanks for the answer. There's an example plot below. Both options you mention seemed plausible and I will do my research and possibility try them. However, I'm not interested in observing if one approach is a lag of the other but instead if one time-series (blue line) follows the same...
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    How to statistically compare the trend similarity of two distinct time series?

    The trend is similar but the values are different due to the different sensibilities of the sensors. Basically what I want to test is the increase and decrease of two NDVI time series through time. When A increases in a time interval will B increase? and when A decrease B decrease? We would...
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    How to statistically compare the trend similarity of two distinct time series?

    I have two NDVI (Normalized Difference Vegetation Index) time series from the same area but collected with different sensors. Since the sensors have different sensibilities to NDVI and data collection intervals I need to compare if both time series have similar trends. I tried the t-test and...
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    How to show in a simplify scientific way the post-hoc test of 4 variables involved, when 3 by 3 variables are significant?

    In a multi-factorial experiment involving 4 variables (species(2x), temperature(2x), nutrients(2x) and light(3x)), 3 by 3 variables, for example, "species", "temperature" and "nutrients" are significant instead of the expected significance of the 4 variables together. I end up with 12 post-hoc...