I have 20 years of data. I want to find the linear trend of the %s as a single number. EG if you were to plot the linear trend, there would be a coefficient by which the line increases/ decreases over time.
Google sheets has a trend function, but it's used for creating new data based on predicting trends.
Your question is too vague to answer clearly and precisely for what you want. Are you looking for the formula for the trend line? Just the correlation coefficient? Or a future value based on the info? The slope of the trend line?
What you have described is linear regression. I would suggest browsing the Insert drop down menu for formulas > statistics. There are formulas for each piece of info you want to draw (except creating the formula for you).
An easy and superficial way of obtaining the correlation coefficient and actual formula (and thus slope for linear trend lines), is to use excel. Copy your data table into excel and then create a scatterplot with the table. Go into the settings for the scatter plot and check the box for “trendline”. Then go into the trendline settings for the plot, and you can select which type of regression you want excel to use. You want linear. Towards the bottom of that menu, you want to check the boxes that say “show formula on chart” and “show R coefficient” or something along those lines. Excel will then print out your formula and coefficient in a text box on the chart. Your slope will be the coefficient of the x variable.
Hope this helps! Regression is a wormhole. I’d love to get more in depth if you’re interested!
NOTE: The outlier for year 2003 will have a significant impact on a linear regression line. Consider removing it from the data to create a line that will be more accurate for future predictions.
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Im a beginner in time series analyses.
I need help finding the SARIIMA(p,d,q,P,D,Q,S) parameters.
This is my dataset. Sampletime 1 hour. Season 24 hour.
S=24
Using the adfuller test I get p = 6.202463523469663e-16. Therefor stationary.
d=0 and D=0
Plotting ACF and PACF:
Using this post:
https://arauto.readthedocs.io/en/latest/how_to_choose_terms.html
I learn to "start counting how many “lollipop” are above or below the confidence interval before the next one enter the blue area."
So looking at PACF I can see maybe 5 before one is below the confidence interval. Therefor non seasonal p=5 (AR).
But I having a hard time finding the q - MA parameter from the ACF.
"To estimate the amount of MA terms, this time you will look at ACF plot. The same logic is applied here: how much lollipops are above or below the confidence interval before the next lollipop enters the blue area?"
But in the ACF plot not a single lollipop is inside the blue area.
Any tips?
There are many different rules of thumb and everyone has own views. I would say, in your case you probably do not need the MA component at all. The rule with the lollipop refers to ACF/PACF plots that have a sharp cut-off after a certain lag, for example in your PACF after the second or third lag. Your ACF is trailing off which can be an indicator for not using the MA component. You do not have to necessarily use it and sometimes the data is not suited for an MA model. A good tip is to always check what pmdarima’s auto_arima() function returns for your data:
https://alkaline-ml.com/pmdarima/tips_and_tricks.html
https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html
Looking at you autocorrelation plot you can clearly see the seasonality. Just because the ADF test tells you it is stationary does not mean it necessarily is. You should at least check if you model works better with seasonal differencing (D).
I have a explanatory variable x and a response variable y. I am trying to find which power of the feature i should train with. You can ignore the colors for my question. the scatter data is from the sensor and the line plot is the theoretical curve from the lab, which you can also ignore for my question.
For this answer I understand you want to obtain some polynomial curve going through the croissant shaped zone where points are dense.
Also I assume that the independent variable is on the horizontal axis, while the dependent is on the vertical one. Otherwise as you can see from the blue line, there is no functional that could give you this.
Now to select the degree of polynomial you can use stepwise regression.
This is about running the regression with more or less features one at a time (i.e decrease or increase the degree of polynomial in this case), and calculating a score such as AIC, BIC, or even adjusted R2 to assess if it's worth it or not to add or remove this feature.
I was looking at many time-series predictions at google image (such as these ones) and I noticed a regular shift in predicted vs actual values. A naive thing that comes to mind is to shift back the predicted curve to have better results. Is this ok to do?
for example, please look at the following figure,
I am able to calculate the forward propagation scores .Could any one provide me the backward propagation calculation for the values provided here .
Also Please explain how the lines in graph are drawn and how it changes when "start repeated parameter update" is clicked.
Please provide sample calculation/explanation for the default values when you load the page.
http://vision.stanford.edu/teaching/cs231n-demos/linear-classify/
Below shows the graph for computation (i am not sure its 100% correct)
formula for gradient loss calculation is is
I have multiple time-series data, plotted as shown below:
Is there a better plotting type that can show the curves of a,b,c, and d in a clearer way? Is there any other plot types where it is easy to see the "twisted" points?
I see two options:
1)
You do some subplots.
pylab_examples example code: subplots_demo.
2)
You let all curves on the same plot. And you apply a rolling mean (also named moving average) on each curve.
Rolling Windows
One more advice.
You can limit your y axis on the graph. Space on the graph will be better optimized
plt.ylim([0,20])