def predict_trend(self):
object_type = 'PyTrends.trend_ratio'
# Pull and prep data
conn = sqlite3.connect("db.sqlite3")
df = pd.read_sql_query(
"select date as ds, btc_usd, buy_bitcoin, trend_ratio from crypto_track_pytrends;",
conn)
df['y'] = df['trend_ratio']
df['ds'] = pd.to_datetime(df['ds'], errors='coerce')
# Get prior 'y' value to fill in nan
df['y-1'] = df.y.shift(1)
df['y_nan'] = df.y.isna()
df.y.fillna(df['y-1'], inplace=True)
df.drop(['y-1'], axis=1, inplace=True)
# Instantiate a Prophet object
future_trend = Prophet(df, 'Trend Ratio')
# Change default attributes as analyzed in Trend Prediction.ipynb notebook.
future_trend.weekly_seasonality = True
future_trend.training_years = 6
future_trend.changepoint_prior_scale = 0.05
future, train, model = future_trend.predict(self.prediction_days)
# Create predictions and load table.
return_message = self.dbload_prophet(df=future,
object_type=object_type)
return return_message
