Table of Contents
- 1 How do you select data sets?
- 2 What are the ways at looking in a set of data history?
- 3 Why use K means for time series data part one?
- 4 Can we cluster time series data?
- 5 What is the best way to compare the patterns of data?
- 6 What is the best model to model time series data?
- 7 What is a time series in statistics?
How do you select data sets?
The dataset should be rich enough to let you play with it, and see some common phenomena. In other words, it must have at least a few thousand rows (> 3.5 − 4K), and at least 20 − 25 columns. Of course, larger is welcome. The dataset should have a reasonable mix of both continuous and categorical variables.
What are the ways at looking in a set of data history?
It is far easier to answer interesting questions of data sets that have not already been analyzed….
- Keep up with media that make use of data.
- Listen to prominent voices in the open data space.
- Request data that’s never seen the light.
- Use metadata to your advantage.
What is time series similarity in data mining?
When treating time series, the similarity between two sequences of the same length can be calculated by summing the ordered point-to-point distance between them (Fig. 3). In this sense, the most used distance function is the Euclidean Distance [13], corresponding to the second degree of general L p -norm [41].
Why use K means for time series data part one?
We can take a normal time series dataset and apply K-Means Clustering to it. This will allow us to discover all of the different shapes that are unique to our healthy, normal signal. We then can take new data, predict which class it belongs to, and reconstruct our dataset based on these predictions.
Can we cluster time series data?
Clustering, which is one of the most important concepts of data mining, defines its structure by separating unlabeled data sets into homogeneous groups. Many general-purpose clustering algorithms are used for the clustering of time-series data, either by directly or by evolving.
How do you win kaggle competitions?
In this post, I’m going to share my tips for Kaggle success.
- Be persistent.
- Spend time on data preparation and feature engineering.
- Don’t ignore domain specific knowledge.
- Pick your competitions wisely.
- Find a good team.
- Other philosophies.
- In summary: persistence and learning.
What is the best way to compare the patterns of data?
If you are interested in comparing the patterns, a very simple approach would be Pearson’s correlation. Keep in mind that this will not compare the actual values but the patterns (i.e. if the values have similar fluctuations with the years, so for example time-series [1 2 3 4] would have higher correlation with [5 6 7 8] than with [1 1 2 2])
What is the best model to model time series data?
Moving average The moving average model is probably the most naive approach to time series modelling. This model simply states that the next observation is the mean of all past observations. Although simple, this model might be surprisingly good and it represents a good starting point.
What are the different methods of time series analysis and forecasting?
The Complete Guide to Time Series Analysis and Forecasting 1 Autocorrelation. 2 Seasonality. 3 Stationarity. 4 Modelling time series. 5 Moving average. 6 Double exponential smoothing. 7 Tripe exponential smoothing.
What is a time series in statistics?
In a time series, time is often the independent variable and the goal is usually to make a forecast for the future. H o wever, there are other aspects that come into play when dealing with time series. Is it stationary? Is there a seasonality?