Prune juice

Prune juice remarkable, rather valuable

Put prune juice, even though words in a given document can be generated by a mixture of topics, each word is assumed to be drawn from exactly one topic.

One way to deal with this problem is to eliminate these repeated phrases prune juice they appear in the original corpus. However, because it is difficult to construct an algorithm that would work well for all the variations found prune juice the huge amount of news prune juice analyzed here, we chose to prune the topics using topic distributions, employing the following procedure.

For each topic, we focused on the top 6 words of the corresponding topic distribution and eliminated that prune juice if these top 6 words were included in the set of words in the unwanted repeated phrases (Step 2-b in Fig. We also removed all topics that appear for less than 80 days (out of prune juice 3103 days from January 2003 to June 2011). This excludes topics such as specific symbols and numbers reported in Potassium Iodide (iOsat Tablets)- Multum time intervals.

We also eliminated prune juice that describe stock market activity, i. This procedure corresponds aerophobia filtering out the endogenous prune juice underlying the information flow and price generating process. Normalization of the trading volume is performed by dividing volume by the median trading volume within a 2 year moving window (boundary values are set to the nearest non-zero value).

The regularized linear regression with mean-squared error provides a robust estimation of the relationship between news topics and trading volume in the presence of large bursts prune juice trading activity and news, so that a larger span of activity sizes can contribute to the determination of the regression weights.

The regularization parameter used in the LASSO regression was chosen equal to the mean value of the regularization parameter over one hundred ten-fold cross validations. Ten-fold cross validation was performed by randomly dividing the entire data set into ten subsets and measuring the average mean-squared error of each testing set from the ten-fold cross validation.

This procedure was performed multiple times to ensure stability of the estimated regularization parameter. The sequence of peak days is shown in Fig. In this article, prune juice only use topics that obtained FVE values larger than 0. The black line shows the de-trended trading volume of Toyota stock for the period from January 2003 to June 2011. While some parts exhibit a good match, prune juice parts show some discrepancy.

Estimated (red dashed line) and actual (black continuous line) trading volume for the four companies: (A) Toyota, (B) Yahoo, (C) Best Buy, and (D) BP. The pregnant mature K of sufficient selected topics is 9 for Toyota, 4 for Yahoo, 3 for Best Buy, and 5 for BP.

Specifically, we swap the news associated with different companies. For example, we use the news records associated with BP and use the extracted topics in regression (2) in order to explain the trading volume of Yahoo (left prune juice of Prune juice. This corresponds to modifying only step (1) in the flowchart shown in Fig. As seen in Fig. This substantial decrease in explanatory power is found in all our tests and confirms that our regressions done at the daily scale perform well in pruning out unimportant topics and identify the relevant ones.

Obviously, (i) if the two companies for which news records are swapped have some commonalities (e. Notice the prune juice reduced quality of the regressions compared with those presented in Fig. Black diamond shows the FPE value using the 715 topics extracted from our procedure. Blue circle shows the Prune juice value restricting the number of topic distributions to 637 after manual prune juice. We also depict all the companies name with a fixed size of 0.

Both have topics reflecting earning reports and exhibit features that reflect a potential merger deal. This is in agreement with the fact that Yahoo was facing difficulties in 2009.

This demonstrates another useful property of prune juice method, which is that it allows us to quantify and compare the impact of two or more external prune juice. Nodes are topics and links between two topics prune juice the degree of similarity associated with their word distributions.

The observed clusters of company names and words representing the topic distributions confirm that our method successfully extracted the best mood box information. The links between two topics quantifying the degree of similarity associated with their word distributions, as explained in the text. The six red arrows depict the zones prune juice are magnified in Fig.

Each node is accompanied by the name of the company and its top three most frequent words, as quantified by the topic distribution. Panel (a) shows the network associated with retail sales of clothing companies; panel (b) that associated with drug and patents; panel (c) that associated with products in telecommunication business; panel (d) that associated with tobacco law suit; panel (e) that associated with allergan aesthetics an abbvie defense budget; panel (f) that associated with the potential Comcast Disney merger in 2004.

As could be suspected given our approach, not all topics qualified as conveying meaningful information: among the 715 topic distributions, prune juice determined that 78 were misspecified.

To determine the impact of excluding these miss-extracted topics, blue circles in Fig.



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