Ghostwriting programming and ghostwriting machine learning models are
based on different machine learning models, using a large number of characteristic variables, to predict and study the fluctuation of the underlying asset price, and to evaluate the prediction effect. Machine learning models include, but are not limited to, classic learning models such as XGBoost, GBDT, and LSTM. Assets to be studied include: stocks, bonds, commodities and other configurable assets. Feature variables include macroeconomic variables, industry variables, target price series, etc. For rebar in bulk commodities, we will provide variety-specific characteristic variables such as supply, demand, cost, etc. Contestants need to take into account the characteristics of time series in the data and the organization of data between different frequencies, and use effective feature extraction methods to build a prediction model for target price fluctuations. We will give specific asset indices and possible characteristic variables, and contestants need to explore the prediction models of these assets.
Evaluation index:
We use the uniformed root mean squared error (URMSE) to measure the prediction error of a single sequence: the
contestant predicts the sequence of Y1, Y2, Y3, and steel, and calculates URMSE1 and URMSE2 respectively. , URMSE3, URMSE4, the final total evaluation index=(URMSE1+URMSE2+URMSE3+URMSE4)/4, which is sorted from low to high according to the total evaluation index.
Data description
1. X series _train.xls, X series _test.xls, steel X_train.xls, and steel data _test.xls provide characteristic variables such as macroeconomic variables and industry variables of the training set and test set, and the players build their own valid feature.
2. Y_train.xls contains 4 sheets, which represent the price fluctuations of Y1, Y2, Y3, and steel in the training set in different time periods.
3. Sample submission:
3.1. Submit a txt file with UTF-8 without BOM encoding, and submit a total of one txt file.
3.2. Y1, Y2, Y3, steel price forecast is divided into four modules, the beginning of each module is identified by a line of Y1, Y2, Y3, iron string respectively.
3.3. The prediction must be made according to the given time point. The date and price are separated by \t, and there can be no missing data or extra data.
Format:
Y1
date1 price
date2 price
...
Y2
date1 price
...
Y3
...
iron
...
3.4 For the specific format, please refer to submit_sample.txt (Note: submit_sample.txt only gives the date, please put in each line when submitting date followed by \t and the corresponding predicted value)
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