wjhgq
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
from utils.getPublicData import *
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from utils.predict import predict_future_values # Use the new function
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import csv
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import os
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import datetime
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import pandas as pd
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def getTopicCreatedAtandpredictData(topic):
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createdAt = {}
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for i in articleList:
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if i[14]==topic:
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if i[7] in createdAt.keys():
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createdAt[i[7]] += 1
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else:
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createdAt[i[7]] = 1
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for i in commentList:
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if i[9]==topic:
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if i[1] in createdAt.keys():
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createdAt[i[1]] += 1
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else:
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createdAt[i[1]] = 1
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# Use the improved time series prediction approach
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predictions = predict_future_values(createdAt, forecast_days=5)
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# Merge historical data and predictions
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combined_data = {**createdAt, **predictions}
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combined_data = {k: combined_data[k] for k in sorted(combined_data, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
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print(list(combined_data.keys()), list(combined_data.values()))
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return list(combined_data.keys()), list(combined_data.values())
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