adding files

Pipfile

@@ -0,0 +1,17 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+statsmodels = "*"
+pandas = "*"
+numpy = "*"
+matplotlib = "*"
+gradio = "*"
+zipp = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.9"

app.py

@@ -0,0 +1,122 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
+from model import PredictionModel
+
+data_path = './data/data_daily.csv'
+damped_model_path = './models/damped_hw_model.pkl'
+model_path = './models/hw_model.pkl'
+
+months = {1: [31, "2022-01-31"], 2: [59, "2022-02-28"], 3: [90, "2022-03-31"], 4: [120, "2022-04-30"], 5: [151, "2022-05-31"], 6: [181, "2022-06-30"], 7: [212, "2022-07-31"], 8: [243, "2022-08-31"], 9: [273, "2022-09-30"], 10: [304, "2022-010-31"], 11: [334, "2022-11-30"], 12: [365, "2022-12-31"]}
+
+damped_model = PredictionModel(damped_model_path, data_path)
+model = PredictionModel(model_path, data_path)
+
+def plot_daily_chart(result, month):
+    fig, axes = plt.subplots(2,2, figsize=(15,15))
+
+    axes[0][0].plot(result.ds, result.y, label='Train')
+    axes[0][0].plot(result.ds, result.yhat, label='yhat')
+    axes[0][0].plot(result.ds, result.upper_bound, label='Upper Bound')
+    axes[0][0].plot(result.ds, result.lower_bound, label='Lower Bound')
+    axes[0][0].set_xticklabels(result.ds.dt.date, rotation=45)
+    axes[0][0].legend(loc='best')
+    axes[0][0].set_title("Daily Forecast")
+    axes[0][0].set_xlabel("Date")
+    axes[0][0].set_ylabel("Receipt Count")
+    axes[0][0].grid(True)
+
+    monthly_result = result.drop('ds', axis=1).groupby(['year', 'month']).sum().reset_index()
+    monthly_result['monthly'] = monthly_result['year'].astype(str) + '-' + monthly_result['month'].astype(str)
+    monthly_result['y'].iloc[-int(month):] = np.nan
+    monthly_result['yhat'].iloc[:12] = np.nan
+    monthly_result['upper_bound'].iloc[:12] = np.nan
+    monthly_result['lower_bound'].iloc[:12] = np.nan
+
+    axes[1][0].plot(monthly_result.monthly, monthly_result.y, label='Train')
+    axes[1][0].plot(monthly_result.monthly, monthly_result.yhat, label='yhat')
+    axes[1][0].plot(monthly_result.monthly, monthly_result.upper_bound, label='Upper Bound')
+    axes[1][0].plot(monthly_result.monthly, monthly_result.lower_bound, label='Lower Bound')
+    axes[1][0].set_xticklabels(monthly_result.monthly, rotation=45)
+    axes[1][0].legend(loc='best')
+    axes[1][0].set_title("Monthly Forecast")
+    axes[1][0].set_xlabel("Date")
+    axes[1][0].set_ylabel("Receipt Count")
+    axes[1][0].grid(True)
+    
+    axes[0][1].plot(result.ds, result.y, label='Train')
+    axes[0][1].plot(result.ds, result.damped_yhat, label='yhat')
+    axes[0][1].plot(result.ds, result.damped_upper_bound, label='Upper Bound')
+    axes[0][1].plot(result.ds, result.damped_lower_bound, label='Lower Bound')
+    axes[0][1].set_xticklabels(result.ds.dt.date, rotation=45)
+    axes[0][1].legend(loc='best')
+    axes[0][1].set_title("Damped Model - Daily Forecast")
+    axes[0][1].set_xlabel("Date")
+    axes[0][1].set_ylabel("Receipt Count")
+    axes[0][1].grid(True)
+
+    monthly_result['damped_yhat'].iloc[:12] = np.nan
+    monthly_result['damped_upper_bound'].iloc[:12] = np.nan
+    monthly_result['damped_lower_bound'].iloc[:12] = np.nan
+
+    axes[1][1].plot(monthly_result.monthly, monthly_result.y, label='Train')
+    axes[1][1].plot(monthly_result.monthly, monthly_result.damped_yhat, label='yhat')
+    axes[1][1].plot(monthly_result.monthly, monthly_result.damped_upper_bound, label='Upper Bound')
+    axes[1][1].plot(monthly_result.monthly, monthly_result.damped_lower_bound, label='Lower Bound')
+    axes[1][1].set_xticklabels(monthly_result.monthly, rotation=45)
+    axes[1][1].legend(loc='best')
+    axes[1][1].set_title("Damped Model - Monthly Forecast")
+    axes[1][1].set_xlabel("Date")
+    axes[1][1].set_ylabel("Receipt Count")
+    axes[1][1].grid(True)
+
+    fig.tight_layout()
+    
+    return fig
+
+
+def cal_result(alpha, month):
+    alpha = float(alpha)
+    period = months[int(month)][0]
+    train_data_len = model.df.shape[0]
+    yhat, intervals = model.predict(period, alpha)
+    damped_yhat, damped_intervals = damped_model.predict(period, alpha)
+
+    START = "2021-01-01"
+    END = months[int(month)][1]
+    
+    result = pd.DataFrame({
+        'ds': pd.date_range(START, END, freq='D'),
+        'y': list(model.df.receipt_count.values) + [np.nan] * period, 
+        'yhat': [np.nan] * train_data_len + list(yhat),
+        'upper_bound': [np.nan] * train_data_len + list(intervals[1]),
+        'lower_bound': [np.nan] * train_data_len + list(intervals[0]),
+        'damped_yhat': [np.nan] * train_data_len + list(damped_yhat),
+        'damped_upper_bound': [np.nan] * train_data_len + list(damped_intervals[1]),
+        'damped_lower_bound': [np.nan] * train_data_len + list(damped_intervals[0])
+    })
+
+    result['year'] = result.ds.dt.year
+    result['month'] = result.ds.dt.month
+
+    daily_chart = plot_daily_chart(result, month)
+    
+    return daily_chart
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        alpha = gr.Dropdown(choices=["0.1", "0.05", "0.01"], label="Significance level", info="The significance level for the confidence intervals.", value="0.05", name="alpha")
+        month = gr.Dropdown(choices=["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"], label="Forecast period", info="The number of months to forecast.", value="12", name="period")
+    
+    submit = gr.Button(text="Submit", label="Submit", type="submit")
+    
+    daily = gr.Plot()
+
+    submit.click(fn=cal_result, inputs=[alpha, month], outputs=daily)
+
+
+demo.launch()

data/data_daily.csv

@@ -0,0 +1,366 @@
+# Date,Receipt_Count
+2021-01-01,7564766
+2021-01-02,7455524
+2021-01-03,7095414
+2021-01-04,7666163
+2021-01-05,7771289
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model.py

@@ -0,0 +1,31 @@
+import pickle
+import pandas as pd
+import numpy as np
+
+class PredictionModel:
+    def __init__(self, model_path, data_path):
+        self.model_path = model_path
+        self.data_path = data_path
+        self.load_model()
+        self.load_data()
+    
+    def load_model(self):
+        with open(self.model_path, 'rb') as f:
+            self.model = pickle.load(f)
+    
+    def load_data(self):
+        self.df = pd.read_csv(self.data_path)
+        self.df.columns = ['date', 'receipt_count']
+
+    def predict(self, period, alpha=0.05):
+        yhat = self.model.forecast(period)
+        intervals = self.compute_confidence_intervals(alpha, yhat)
+        return yhat, intervals
+
+    def compute_confidence_intervals(self, alpha, yhat):
+        residuals = self.df.receipt_count - self.model.fittedvalues
+        z = abs(np.percentile(np.random.standard_normal(10000), [100 * alpha/2, 100 * (1 - alpha/2)]))
+        std_residual = residuals.std()
+        interval_upper = yhat + z[1] * std_residual * np.sqrt(1 + 1/len(self.df))
+        interval_lower = yhat - z[1] * std_residual * np.sqrt(1 + 1/len(self.df))
+        return [interval_lower, interval_upper]

models/damped_hw_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe2517c3cb675a082dd0649e4d027ec4bb99019a2cfa4e9b1c92fa7b3292d050
+size 28498

models/hw_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b54f6bc85ef9993b414a03a8ea140212bce994e5a044fa91425c3c04568ad229
+size 28456