Non strict read write and think

Strict Mode imposes a layer of constraint on JavaScript — intended to protect you from the more perilous aspects of the language. While researching this article I wrote 38 tests covering all the Strict Mode rules as defined in the ES5 specification. You can see how your favorite browser shapes up by clicking here. The code for each test is reproduced at the end of the article as an aid to understanding the specification. Looks like we are good to go! Methods to Check Stationarity The next step is to determine whether a given series is stationary or not and deal with it accordingly.

This section looks at some common methods which we can use to perform this check. Visual test Consider the plots we used in the previous section. We were able to identify the series in which mean and variance were changing with time, simply by looking at each plot.

Similarly, we can plot the data and determine if the properties of the series are changing with time or not. It is better to confirm the observations using some statistical tests. Statistical test Instead of going for the visual test, we can use statistical tests like the unit root stationary tests.

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Unit root indicates that the statistical properties of a given series are not constant with time, which is the condition for stationary time series.

Here is the mathematics explanation of the same: Suppose we have a time series: In order to calculate yt we need the value of yt-1, which is: This is knows as unit root in a time series. We know that for a stationary time series, the variance must not be a function of time. Below are the two of the most commonly used unit root stationary tests: It can be used to determine the presence of unit root in the series, and hence help us understand if the series is stationary or not.

The null and alternate hypothesis of this test are: The series has no unit root. If we fail to reject the null hypothesis, we can say that the series is non-stationary.

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This means that the series can be linear or difference stationary we will understand more about difference stationary in the next section.

The results of our test for this particular series are: Results of Dickey-Fuller Test: If the test statistic is less than the critical value, we can reject the null hypothesis aka the series is stationary. When the test statistic is greater than the critical value, we fail to reject the null hypothesis which means the series is not stationary.

This confirms our original observation which we initially saw in the visual test. The authors of the KPSS test have defined the null hypothesis as the process is trend stationary, to an alternate hypothesis of a unit root series. We will understand the trend stationarity in detail in the next section.

The process is trend stationary. The series has a unit root series is not stationary. For the air passengers dataset, here are the results: If the test statistic is greater than the critical value, we reject the null hypothesis series is not stationary.

If the test statistic is less than the critical value, if fail to reject the null hypothesis series is stationary. For the air passenger data, the value of the test statistic is greater than the critical value at all confidence intervals, and hence we can say that the series is not stationary.

I usually perform both the statistical tests before I prepare a model for my time series data. It once happened that both the tests showed contradictory results.

One of the tests showed that the series is stationary while the other showed that the series is not! I got stuck at this part for hours, trying to figure out how is this possible.>> When [strict locking] is enabled, the NFS server locks the file on every read and write.

From what I’ve read, “strict locking” means that each read/write will check whether a range is locked before performing the operation (or fail if the range is locked); not that the read/write .

Strict Consistency (Last Time) • Any execution is the same as if all read/write ops were executed in order of wall-clock time at which they were issued. If you want to read the file in arbitrary-sized chunks (say, 1K or 4K), you need to write error-handling code to catch the case where only part of the bytes encoding a single Unicode character are read .