Social media offers an invaluable resource for understanding one's customers. However, going through thousands of social media data (e.g., comments) manually in order to interpret their meaning takes a lot of time.Using natural language processing (NLP) techniques, you can automatize the process and uncover the emotions that best describe your [...]
Read MoreIn today's fast-paced world, gaining valuable insights from vast amounts of data has emerged changer for companies across virtually all industries. At the heart of this process lies the ability to collect, organize, and analyze vast quantities of information – often called 'big data.' This term encompasses structured and unstructured [...]
Read More
Within our fast-changing economy, enormous amounts of data are created each day. We are online everywhere and everyday. Doing so we leave a large trace of data which is higly valuable for companies. Specifically, these data traces allow companies to grasp the changes and characteristics just as they are happening. It is, therefore, not surprising that data is now a […]
As you may know, individual data points, just for themselves, are more or less meaningless. However, if you have a more extensive collection of such data points, you can derive all sorts of precious information from them. This information you can use to power innovations and enhance decisions. When it comes to data analysis, we generally differentiate between two kinds […]
Digitalization is the most imporant trend that is transforming both, society and companies on a global scale. This is due to the fact that, digitalization removed the locality of information. Removing their locality made it possible for flows of information to become non-linear. That means information is now simultaneously flowing around between many points (e.g., computers). At this point, the […]
To quote Jack Dorsey ” Most of the data is meaningless until someone adds some interpretation…“ To visualize this quote’s meaning, let’s look at today’s stock price of Example Company Inc: Today Example Company Inc. lists at 101R/share on the JSE. For itself, this data point is pretty meaningless. However, generally, datapoints become more useful when we collect and structure […]
Here is a simple example of an ARIMA model with pricing data. This is just an example to show the basic code used for ARIMA. Statistical tests in order to choose the appropriate model/lags are not included. import osos.chdir(r”C:\Users\haderer\Documents\python”)cwd= os.getcwd()print(“Current working directory is:”, cwd) import numpy as npimport sysnp.set_printoptions(threshold=sys.maxsize)import pandas as pdfrom matplotlib import pyplot as pltfrom statsmodels.tsa.stattools import adfullerfrom […]
Dataset: Output Graph /ADF: Output graph:
The null hypothesis of the dickey fuller test is that your data is a random walk. Sample Regression : Now we could use our stock returns as an example Y = return today a = lets asume its 0 x= return yesterday b = slope of returns regression line E = White noise Now we test if b (slope of […]
It is not possible to make a forcast of a random walk. Lets have a look at the formula: Example from the stock market: Todays stock price 5 Euro Yesterdays stock price 4 Euro Error (would be the retrun) = 1 Euro
Autocorrelation measures the correlation of a time series with itself. Therefore, when analyzing autocorrelation, the correlated variables are the same, measured on different dates. (eg. – 1 month = 1 lag) An autocorrelation different from 0 implies that a dataset can be forecast out of its past. For example, let’s assume an EM Index is autocorrelated. Also, you have the […]