使用 sklearn RandomForestRegressor 时，我的数据帧的 x 值是多少？

Question

I'm working on a big data project for my school project. My dataset looks like this: https://github.com/gindeleo/climate/blob/master/GlobalTemperatures.csv

I'm trying to predict the next values of "LandAverageTemperature".

I've asked another question about this topic earlier.(its here: How to predict correctly in sklearn RandomForestRegressor? ) I couldn't get any answer for that question.After not getting anything in my first question and then failing for another day, I've decided to start from scratch.

Right now, I want to know which value is in my dataset is "x" to make the prediction correctly. I read that y is a dependent variable which that I want to predict and x is the independent variable that I should use as "predictor" to help the prediction proccess. In that case my y variable is "LandAverageTemperature". I don't know what the x value is. I was using date values for x at first but I'm not sure that is true at the moment.

And if I shouldn't use RandomForestRegressor or sklearn (I've started with spark to this project) for this dataset please let me know. Thanks in advance.

Answer 1

You only have one variable ( LandAverageTemperature ), so obviously that's what you're going to use. What you're looking for is the df.shift() function, which shifts your values. With this function, you'll be able to add columns of past values to your dataframe . You will then be able to use t 1 month/day ago , t 2 months/days ago , etc, as predictors of another day/month's temperature.

You can use it like this:

for i in range(1, 15):
    df.loc[:, 'T–%s'%i] = df.loc[:, 'LandAverageTemperature'].shift(i)

Your columns will then be temperature , and temperature at T-1 , T-2 , for up to 14 time periods.

For your question about what is a proper model for time series forecasting, it would be off-topic for this site, but many resources exist on https://stats.stackexchange.com .

Answer 2

In general case you can use for X feature matrix all data columns excluding your target column . But in your case there is several complications:

• You have missed (empty) data in most of the columns for many years. You can exclude such rows/years from train data. Or exclude columns with missed data (which will be almost all of your columns and it's not good).
• Regression model can't use date fields directly, you should traislate date field to some numerical field(s), "months past first observation", for example. Something like (year-1750)*12 + month . Or/and you can have year and month in separate columns (it's better if you have some "seasonality" in your data).
• You have sequental time data here, so may be you should not use simple regression. Use some ARIMA/SARIMA/SARIMAX and so on so-called Time-Series models which predicts target data sequentially one value by another, month after month in your case. It's a hard topic for learning, but you should definitely take a look at TS because you will need it some time in the future if not today.