March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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266
1
부
모델 구축
값
시간
그림
9-8
이동 평균 그래프
MSE
는
49
,
MAE
는
5
.
5
를 얻었습니다. 예측이 조금 향상되었네요. 하지만 이 방식은 트렌드나
계절성을 고려하지 않으므로 약간의 분석을 통해 결과를 더 향상할 수 있습니다.
9.2.4
개선된 이동 평균 방법
이 시계열의 계절성은
365
일이므로 시간
t
의 값에서
t
-
365
의 값을 빼서 트렌드와 계절성을
줄일 수 있습니다. 이렇게 하면 그래프가 평평해집니다. 코드는 다음과 같습니다.
diff_series = (series[365:] - series[:-365])
diff_time = time[365:]
이제 이 값에서 이동 평균을 계산해 이전 값에 더합니다.
diff_moving_avg = \
moving_average_forecast(diff_series, 50)[split_time - 365 - 50:]
diff_moving_avg_plus_smooth_past = \
moving_average_forecast(series[split_time - 370:-360], 10) + \
diff_moving_avg
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.