獲取模型訓練數據和預測加密貨幣價格的問題

Question

我正在嘗試使用 Facebook 的先知模型和來自雅虎財經的加密貨幣價格數據來預測未來的價格。 我已經導入了所有庫，定義了從 Yahoo Finance 獲取歷史數據的函數，但是在獲取數據並訓練模型后，當我嘗試運行代碼以可視化數據時，我得到一個 ValueError: ValueError: All arguments should have the same長度。 參數y的長度是 4，而前面的參數 ['ds'] 的長度是 398。我將把整個代碼放在下面。 請幫我。

from tqdm import tqdm
import pandas as pd
from prophet import Prophet
import yfinance as yf
from datetime import datetime, timedelta
import plotly.express as px
import numpy as np

def getData(ticker, window, ma_period):
    """
    Grabs price data from a given ticker. Retrieves prices based on the given time window; from now
    to N days ago.  Sets the moving average period for prediction. Returns a preprocessed DF
    formatted for FB Prophet.
    """
    # Time periods
    now = datetime.now()

    # How far back to retrieve tweets
    ago = now - timedelta(days=window)

    # Designating the Ticker
    crypto = yf.Ticker(ticker)

    # Getting price history
    df = crypto.history(start=ago.strftime("%Y-%m-%d"), end=now.strftime("%Y-%m-%d"), interval="1d")
    
    # Handling missing data from yahoo finance
    df = df.reindex(
        [df.index.min()+pd.offsets.Day(i) for i in range(df.shape[0])],
        fill_value=None
    ).fillna(method='ffill')
    
    # Getting the N Day Moving Average and rounding the values
    df['MA'] = df[['Open']].rolling(window=ma_period).mean().apply(lambda x: round(x, 2))

    # Dropping the NaNs
    df.dropna(inplace=True)

    # Formatted for FB Prophet
    df = df.reset_index().rename(columns={"Date": "ds", "MA": "y"})
    
    return df



def fbpTrainPredict(df, forecast_period):
    """
    Uses FB Prophet and fits to a appropriately formatted DF. Makes a prediction N days into 
    the future based on given forecast period. Returns predicted values as a DF.
    """
    # Setting up prophet
    m = Prophet(
        daily_seasonality=True, 
        yearly_seasonality=True, 
        weekly_seasonality=True
    )
    
    # Fitting to the prices
    m.fit(df[['ds', 'y']])
    
    # Future DF
    future = m.make_future_dataframe(periods=forecast_period)
        
    # Predicting values
    forecast = m.predict(future)

    # Returning a set of predicted values
    return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

def visFBP(df, forecast):
    """
    Given two dataframes: before training df and a forecast df, returns
    a visual chart of the predicted values and actual values.
    """
    # Visual DF
    vis_df = df[['ds','Open']].append(forecast).rename(
        columns={'yhat': 'Prediction',
                 'yhat_upper': "Predicted High",
                 'yhat_lower': "Predicted Low"}
    )

    # Visualizing results
    fig = px.line(
        vis_df,
        x='ds',
        y=['Open', 'Prediction', 'Predicted High', 'Predicted Low'],
        title='Crypto Forecast',
        labels={'value':'Price',
                'ds': 'Date'}
    )

    # Adding a slider
    fig.update_xaxes(
        rangeselector=dict(
            buttons=list([
                dict(count=1, label="1m", step="month", stepmode="backward"),
                dict(count=3, label="3m", step="month", stepmode="backward"),
                dict(count=6, label="6m", step="month", stepmode="backward"),
                dict(count=1, label="YTD", step="year", stepmode="todate"),
                dict(count=1, label="1y", step="year", stepmode="backward"),
                dict(step="all")
            ])
        )
    )

    return fig.show()

 
# Getting and Formatting Data
df = getData("SHIB-USD", window=730, ma_period=5)
# Training and Predicting Data
forecast = fbpTrainPredict(df, forecast_period=90)
# Visualizing Data
visFBP(df, forecast)

Answer 1

我使用 plotly 的圖形對象單獨添加每條圖形線。 確保包含 graph_objects: import plotly.graph_objects as go

然后而不是：

# Visualizing results
fig = px.line(
    vis_df,
    x='ds',
    y=['Open', 'Prediction', 'Predicted High', 'Predicted Low'],
    title='Crypto Forecast',
    labels={'value':'Price',
            'ds': 'Date'}
)

您創建空圖並將每個數據列添加為“散點”：

# Visualizing results
fig = go.Figure()
fig.add_scatter(x=vis_df['ds'], y=vis_df['Open'],mode='lines', name="Open")
fig.add_scatter(x=vis_df['ds'], y=vis_df['Prediction'],mode='lines', name="Prediction")
fig.add_scatter(x=vis_df['ds'], y=vis_df['Predicted Low'],mode='lines', name="Predicted Low")
fig.add_scatter(x=vis_df['ds'], y=vis_df['Predicted High'],mode='lines', name="Predicted High")
fig.update_layout(
    title="Crypto Forecast",
    xaxis_title="Date",
    yaxis_title="Price",
)

您的預測似乎仍然存在問題，但我將不得不將其留給其他人。