How to statistically compare two time series? - Cross Validated
If time series x is the similar to time series y then the variance of x-y should be less than the variance of x. We can test this using a one sided F test for variance. If the ratio var(x-y)/var(x) is significantly less than one then then y explains a significant proportion of the variance of x.
How to use Pearson correlation correctly with time series
2015年1月13日 · Also, imagine that two time series are strongly dependent, say moves up together and goes down together, but one undergoing sometimes strong variations and the other one having always mild variations, your Pearson correlation will be rather low unlike the Spearman and Kendall ones (which are better estimates of …
terminology - What are differences between the terms "time series ...
2014年4月13日 · time series: the same subject at different times; think of it as one column with rows corresponding to different time points; panel (longitudinal): many subjects at different times, you have the same subject at different times, and you have many subjects at the same time; think of it as a table where rows are time points, and columns are subjects.
time series - How to characterize abrupt change? - Cross Validated
If the observations of your time series data are correlated with the immediately previous observations, the paper by Chen and Liu (1993)$^{[1]}$ may interest you. It describes a method to detect level shifts and temporary changes in the framework of autoregressive moving-average time series models. [1]: Chen, C. and Liu, L-M. (1993),
time series - Ljung-Box Statistics for ARIMA residuals in R: …
However, in the last 12 months of my time series my model (fit2) seems to be a better fit when adjusted (it was chronically biased, I have added the residual mean and the new fit seems to sit more snugly around the original time series. Here is the example of the last 12 months and MAPE for 12 most recent months for both fits:
time series - Interpreting R's ur.df (Dickey-Fuller unit root test ...
It seems the creators of this particular R command presume one is familiar with the original Dickey-Fuller formulae, so did not provide the relevant documentation for how to interpret the values. I found that Enders was an incredibly helpful resource (Applied Econometric Time Series 3e, 2010, p. 206-209--I imagine other editions would also be ...
time series - Interpretation of correlogram - Cross Validated
2015年11月19日 · On the y axis is the autocorrelation. The x axis tells you the lag. So, if x=1 we are looking at the correlation of December with November, November with October, etc.
time series - ADF vs. DF what is the difference between …
2016年9月15日 · Many financial time series, however, have a more complicated dynamic structure that is captured by a simple AR(1) model. Said and Dickey (1984) augmented the basic autoregressive unit root test to accommodate general ARMA(p, q) models with unknown orders and their test is referred to as augmented Dickey-Fuller (ADF) test.
time series - ARDL/Error Correction Model: long vs. short run ...
2015年1月30日 · In the long run the first differences are taken as zero and the long-run equation reduces to $\gamma_1y+\gamma_2x$=0 which is the long run relationship between the variables.
time series - Interpret Regression Coefficients After various ...
2015年9月3日 · I then integrated both the X1 and the Y1 white nosie series and obtained two new series (X2 and Y2). Following is the result of an OLS model for X2 AND Y2 [ ][4 The resultant regression coefficient is nearly identical (small variation due to 1 …