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import pandas as pd
import matplotlib.pyplot as plt
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from model import Kronos, KronosTokenizer, KronosPredictor
def plot_prediction(kline_df, pred_df):
pred_df.index = kline_df.index[-pred_df.shape[0]:]
sr_close = kline_df['close']
sr_pred_close = pred_df['close']
sr_close.name = 'Ground Truth'
sr_pred_close.name = "Prediction"
close_df = pd.concat([sr_close, sr_pred_close], axis=1)
fig, ax = plt.subplots(1, 1, figsize=(8, 4))
ax.plot(close_df['Ground Truth'], label='Ground Truth', color='blue', linewidth=1.5)
ax.plot(close_df['Prediction'], label='Prediction', color='red', linewidth=1.5)
ax.set_ylabel('Close Price', fontsize=14)
ax.legend(loc='lower left', fontsize=12)
ax.grid(True)
plt.tight_layout()
plt.show()
# 1. Load Model and Tokenizer
tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = Kronos.from_pretrained("NeoQuasar/Kronos-base")
# 2. Instantiate Predictor
predictor = KronosPredictor(model, tokenizer, device="cpu", max_context=512)
# 3. Prepare Data
df = pd.read_csv("./examples/data/XSHG_5min_600977.csv")
df['timestamps'] = pd.to_datetime(df['timestamps'])
lookback = 400
pred_len = 120
x_df = df.loc[:lookback-1, ['open', 'high', 'low', 'close']]
x_timestamp = df.loc[:lookback-1, 'timestamps']
y_timestamp = df.loc[lookback:lookback+pred_len-1, 'timestamps']
# 4. Make Prediction
pred_df = predictor.predict(
df=x_df,
x_timestamp=x_timestamp,
y_timestamp=y_timestamp,
pred_len=pred_len,
T=1.0,
top_p=0.9,
sample_count=1,
verbose=True
)
# 5. Visualize Results
print("Forecasted Data Head:")
print(pred_df.head())
# Combine historical and forecasted data for plotting
kline_df = df.loc[:lookback+pred_len-1]
# visualize
plot_prediction(kline_df, pred_df)
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