Time Series Analysis and Clustering Issues

Answered using OpenAI o1 preview by OpenAI o1

Question:

/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: should_run_async will not call transform_cell automatically in the future. Please pass the result to transformed_cell argument and any exception that happen during thetransform in preprocessing_exc_tuple in IPython 7.17 and above. and should_run_async(code) 歷史數據分析結果: num_1 num_2 num_3 num_4 num_5
count 1895.000000 1895.000000 1895.000000 1895.000000 1895.000000
mean 6.302375 12.295515 18.646966 24.896042 31.127704
std 5.216928 6.459218 7.393696 7.584878 7.464562
min 1.000000 2.000000 3.000000 4.000000 7.000000
25% 2.000000 7.000000 13.000000 20.000000 26.000000
50% 5.000000 12.000000 18.000000 25.000000 32.000000
75% 9.000000 16.000000 24.000000 30.500000 37.000000
max 33.000000 36.000000 41.000000 44.000000 47.000000

         num_6        num_7

count 1895.000000 1895.000000
mean 37.414776 43.673351
std 6.687629 5.321491
min 10.000000 15.000000
25% 33.000000 41.000000
50% 38.000000 45.000000
75% 43.000000 48.000000
max 48.000000 49.000000
/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:559: UserWarning: Could not infer format, so each element will be parsed individually, falling back to dateutil. To ensure parsing is consistent and as-expected, please specify a format. _index = to_datetime(index) /usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) /usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:559: UserWarning: Could not infer format, so each element will be parsed individually, falling back to dateutil. To ensure parsing is consistent and as-expected, please specify a format. _index = to_datetime(index) /usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) /usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:559: UserWarning: Could not infer format, so each element will be parsed individually, falling back to dateutil. To ensure parsing is consistent and as-expected, please specify a format. _index = to_datetime(index) /usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) 時間序列分析預測結果: Empty DataFrame Columns: [forecast] Index: [] /usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals warnings.warn("Maximum Likelihood optimization failed to " /usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at start. return get_prediction_index( /usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception. return get_prediction_index(

ValueError Traceback (most recent call last) <ipython-input-6-07659a6981b1> in <cell line: 2091>() 2089 # 步驟 3: 聚類分析 2090 # 將預測結果添加到 DataFrame -> 2091 forecast_df['cluster'] = KMeans(n_clusters=7, random_state=42).fit_predict(forecast_df) 2092 2093 # 聚類分析結果

4 frames /usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name) 965 n_samples = _num_samples(array) 966 if n_samples < ensure_min_samples: --> 967 raise ValueError( 968 "Found array with %d sample(s) (shape=%s) while a" 969 " minimum of %d is required%s."

ValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required by KMeans.

Answer:

從您提供的錯誤信息來看，您在進行時間序列分析和聚類分析時遇到了一些問題。以下是一些可能的原因和解決方案：

  1. 時間序列預測結果為空：

    • 您的時間序列分析結果顯示為空的 DataFrame，這意味著預測沒有生成任何結果。這可能是由於模型未能收斂或數據格式不正確。
    • 確保您提供給模型的數據是正確的，並且已經進行了適當的預處理（例如，填補缺失值、標準化等）。

  2. KMeans 聚類分析的數據問題：

    • 錯誤信息顯示 KMeans 嘗試對一個空的 DataFrame 進行聚類，這是因為 forecast_df 是空的。
    • 在進行聚類之前，您需要檢查 forecast_df 是否包含有效的預測數據。如果預測結果為空，則無法進行聚類分析。

  3. 數據格式問題：

    • 警告信息中提到的 UserWarning 和 ValueWarning 表示在處理日期時間索引時出現了問題。確保您的時間序列數據具有正確的日期時間格式，並且在進行分析之前已經正確設置索引。

建議的步驟：

  1. 檢查數據：

    • 確保您用於時間序列分析的數據是完整的，並且沒有缺失值。
    • 檢查數據的格式，特別是日期時間的格式。

  2. 調整模型參數：

    • 如果模型未能收斂，您可以嘗試調整模型的參數，或者使用不同的模型來進行預測。

  3. 添加錯誤處理：

    • 在進行聚類分析之前，檢查 forecast_df 是否為空。如果是空的，則跳過聚類分析或提供適當的錯誤信息。

  4. 示例代碼：

    # 檢查預測結果是否為空
if not forecast_df.empty:
    forecast_df['cluster'] = KMeans(n_clusters=7, random_state=42).fit_predict(forecast_df)
else:
    print("預測結果為空，無法進行聚類分析。")

通過這些步驟，您應該能夠找到問題的根源並進行相應的修正。