python
12pip install neuralprophet 3import pandas as pd 4import numpy as np 5import seaborn as sns 6from matplotlib import pylab as plt 7 8%matplotlib inline 9from neuralprophet import NeuralProphet 10from sklearn.metrics import mean_absolute_error 11import warnings 12warnings.filterwarnings("ignore")13 14df = pd.read_csv("/content/kotora.csv")15df["kotora"] = df["kotora"].astype("float64")16df = df.rename(columns={"kotora": "kotora"})17df.ym = pd.to_datetime(df.ym)18df.head()19 20fig, ax = plt.subplots()21a = sns.lineplot(x="ym", y="kotora", data=df)22plt.show()23df = df.rename(columns={"ym": "ds", "kotora": "y"})24df = df[["ds", "y"]]25 26test_length = 1027df_train = df.iloc[:-test_length]28df_test = df.iloc[-test_length:]29 30params = {31 "growth": "linear",32 "changepoints": None,33 "n_changepoints": 10,34 "changepoints_range": 0.9,35 "trend_reg": 0,36 "trend_reg_threshold": False,37 "yearly_seasonality": True,38 "weekly_seasonality": False,39 "daily_seasonality": False,40 "seasonality_mode": "multiplicative",41 "seasonality_reg": 0,42 "n_forecasts": 1,43 "n_lags": 0,44 "num_hidden_layers": 0,45 "d_hidden": None,46 "ar_sparsity": None,47 "learning_rate": None,48 "epochs": 1000,49 "batch_size": None,50 "loss_func": "Huber",51 "train_speed": None,52 "normalize": "auto",53 "impute_missing": True,54}55model = NeuralProphet(**params)56model.fit(df_train,57 validate_each_epoch=True,58 valid_p=0.2,59 freq='M',60 plot_live_loss=True)
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