准确率计算
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workpc
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@ -200,6 +200,19 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
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# 保存预测值
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df_predict.to_csv(os.path.join(dataset,"predict.csv"),index=False)
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df_predict2 = df_predict.copy()
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# 保存到数据库
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if not sqlitedb.check_table_exists('accuracy'):
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sqlitedb.create_table('accuracy', columns="id int,PREDICT_DATE datetime,CREAT_DATE datetime, MIN_PRICE TEXT,MAX_PRICE TEXT,HIGH_PRICE TEXT,LOW_PRICE TEXT,RIGHT_ROTE ")
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existing_data = sqlitedb.execute_query("SELECT * FROM accuracy")
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if not existing_data.empty:
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max_id = existing_data['id'].astype(int).max()
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df_predict2['id'] = range(max_id + 1, max_id + 1 + len(df_predict2))
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else:
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df_predict2['id'] = range(1, 1 + len(df_predict2))
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df_predict2.to_sql(table_name, con=sqlitedb.connect, if_exists='append', index=False)
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# 把预测值上传到eta
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if is_update_eta:
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dates = df_predict['ds'].dt.strftime('%Y-%m-%d')
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143
原油价格预测准确率计算.ipynb
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143
原油价格预测准确率计算.ipynb
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@ -0,0 +1,143 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 43,
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"id": "9daadf20-caa6-4b25-901c-6cc3ef563f58",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(135, 32)\n",
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"(20, 4)\n",
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"(135, 35)\n",
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" ds NHITS Informer LSTM iTransformer TSMixer \\\n",
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"0 2024-05-21 83.607803 85.574699 82.902397 86.689003 83.845596 \n",
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"1 2024-05-22 83.306198 84.577400 82.204903 86.366203 83.341599 \n",
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"2 2024-05-23 82.561203 83.894600 81.447899 85.681198 82.593300 \n",
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"3 2024-05-24 82.007896 81.672203 81.100899 84.773598 82.038101 \n",
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"4 2024-05-27 82.463600 81.498398 81.599297 85.471298 82.187202 \n",
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"\n",
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" TSMixerx PatchTST RNN GRU ... quantile_90 \\\n",
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"0 85.564499 83.900002 60.229801 82.869698 ... 0.0149 \n",
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"1 85.500900 82.637299 60.229698 82.206802 ... 0.0149 \n",
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"2 85.219299 82.346603 60.229698 81.471298 ... 0.0149 \n",
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"3 83.707901 81.840401 60.229599 81.120201 ... 0.0149 \n",
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"4 83.175301 82.142998 60.229698 81.602699 ... 0.0145 \n",
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"\n",
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" quantile_10_price quantile_90_price min_within_quantile \\\n",
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"0 81.993181 84.114909 82.757301 \n",
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"1 80.966342 83.120312 82.173500 \n",
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"2 80.432497 82.572265 81.447899 \n",
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"3 80.907020 83.059412 81.100899 \n",
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"4 81.935165 84.081757 82.010399 \n",
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"\n",
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" max_within_quantile min_model max_model 序号 lowprice highprice \n",
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"0 83.954498 MLPMultivariate DLinear NaN NaN NaN \n",
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"1 83.068497 MLPMultivariate NLinear NaN NaN NaN \n",
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"2 82.452904 LSTM TiDE NaN NaN NaN \n",
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"3 82.933502 LSTM DeepNPTS NaN NaN NaN \n",
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"4 83.266098 DLinear TFT NaN NaN NaN \n",
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"\n",
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"[5 rows x 35 columns]\n",
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" ds NHITS Informer LSTM iTransformer TSMixer \\\n",
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"130 2024-12-03 72.612854 73.548740 73.456590 73.261700 72.531740 \n",
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"131 2024-12-04 73.003940 73.408714 73.738144 73.359000 72.759056 \n",
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"132 2024-12-05 72.435104 73.837450 73.900570 73.456290 72.775955 \n",
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"133 2024-12-06 71.915250 73.683400 73.801330 73.549644 72.517870 \n",
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"134 2024-12-09 72.446610 73.003040 74.081820 73.970030 72.687225 \n",
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"\n",
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" TSMixerx PatchTST RNN GRU ... quantile_90 \\\n",
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"130 72.424950 73.314370 62.063490 73.14000 ... NaN \n",
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"131 71.986694 74.242960 63.026990 73.08031 ... NaN \n",
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"132 72.056160 73.494240 62.319950 73.15901 ... NaN \n",
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"133 71.949066 73.083786 62.659557 73.37916 ... NaN \n",
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"134 71.930230 72.724205 63.025295 73.40915 ... NaN \n",
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"\n",
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" quantile_10_price quantile_90_price min_within_quantile \\\n",
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"130 NaN NaN 72.386850 \n",
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"131 NaN NaN 72.940490 \n",
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"132 NaN NaN 73.387985 \n",
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"133 NaN NaN 73.056526 \n",
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"134 NaN NaN 73.581210 \n",
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"\n",
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" max_within_quantile min_model max_model 序号 lowprice highprice \n",
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"130 73.48625 TCN FEDformer 4.0 71.68 73.93 \n",
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"131 73.58921 TCN FEDformer 3.0 72.25 74.28 \n",
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"132 73.36786 TCN FEDformer 2.0 71.80 72.92 \n",
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"133 73.30574 TCN FEDformer 1.0 70.85 72.19 \n",
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"134 73.06101 TCN FEDformer NaN NaN NaN \n",
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"\n",
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"[5 rows x 35 columns]\n"
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]
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}
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],
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"source": [
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"import sqlite3\n",
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"import os\n",
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"import pandas as pd\n",
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"\n",
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"# 预测价格数据\n",
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"dbfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','jbsh_yuanyou.db')\n",
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"conn = sqlite3.connect(dbfilename)\n",
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"query = 'SELECT * FROM testandpredict_groupby'\n",
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"df1 = pd.read_sql_query(query, conn)\n",
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"df1['ds'] = pd.to_datetime(df1['ds'])\n",
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"conn.close()\n",
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"print(df1.shape)\n",
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"# 最高最低价\n",
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"xlsfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','数据项下载.xls')\n",
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"df2 = pd.read_excel(xlsfilename)[5:]\n",
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"df2 = df2.rename(columns = {'数据项名称':'ds','布伦特最低价':'lowprice','布伦特最高价':'highprice'})\n",
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"df2['ds'] = pd.to_datetime(df2['ds'])\n",
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"\n",
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"print(df2.shape)\n",
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"df = pd.merge(df1,df2,on='ds',how='outer')\n",
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"\n",
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"df = df.reindex()\n",
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"\n",
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"print(df.shape)\n",
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"\n",
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"df.to_csv(os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','123.csv'))\n",
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"# df = df[['ds','min_within_quantile','max_within_quantile']]\n",
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"\n",
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"\n",
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"\n",
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"# 打印数据框的前几行\n",
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"print(df.head())\n",
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"print(df.tail())\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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