The way to Construct a Forecasting Pipeline with TimeCopilot Utilizing Basis Fashions and Automated Anomaly Detection
import os, warnings import numpy as np import pandas as pd import matplotlib.pyplot as plt warnings.filterwarnings(“ignore”) pd.set_option(“show.width”, 160) pd.set_option(“show.max_columns”, 30) print(“numpy:”, np.__version__) import scipy; print(“scipy:”, scipy.__version__) strive: import torch HAS_GPU = torch.cuda.is_available() besides Exception: HAS_GPU = False print(f”GPU obtainable: {HAS_GPU}”) df = pd.read_csv( “https://timecopilot.s3.amazonaws.com/public/information/air_passengers.csv”, parse_dates=[“ds”], ) df[“unique_id”] = df[“unique_id”].astype(str) rng = np.random.default_rng(7) dates = df[“ds”].distinctive();…
