Michael GOM

Global Operation Management - Forecasting Logistics

Focuses on forecasting logistics within operations management, serving as a valuable resource to improve users forecasting skills. Provides training on various forecasting models including SWA, WMA, EMA, Regression Analysis, Holt’s Linear Trend, and Winters Seasonal Method. Each model includes detailed explanations, step-by-step instructions, and real-life examples, enabling users to apply these methods effectively to users own datasets.

Regular time series - Constand Trend(SWA) Watch Tutorial

import json import pandas as pd def get_plot_string(data, r): result = calculate_sma(data, r) # Predict next values predicted_df = sma_predict_next_value(result) plt = plot_sma_data(result, r, predicted_df) buf = io.BytesIO() plt.savefig(buf, format='png') plt.close() buf.seek(0) string = base64.b64encode(buf.read()).decode('utf-8') # output return "data:image/png;base64,"+ string def on_instant(event): try: window_size = int(Element("D1").element.value) json_str = Element("sma_result").element.innerHTML if json_str != '': json_data = json.loads(json_str) df = pd.DataFrame(json_data) df['T'] = pd.to_numeric(df['T'], errors='coerce').astype('int64') df['Yt'] = pd.to_numeric(df['Yt'], errors='coerce').astype('float64') img_data = get_plot_string(df, window_size) Element("out_instant").element.innerHTML = "Plt successed." Element("plot_image").element.src = img_data Element("plot_image").element.style.display = "block" else: Element("out_instant").element.innerHTML = "Please upload csv file." except ValueError: Element("out_instant").element.innerHTML = "Invalid input. Please enter numeric values." Element("button_instant").element.onclick = on_instant

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Regular time series - Constand Trend(WMA) Watch Tutorial

import json import pandas as pd def get_plot_string1(data, r, forecast_periods): result = calculate_wma(data, r) future_values = predict_future_values_wma(result, r, forecast_periods) plt = plot_data_wma(result, r, future_values) buf = io.BytesIO() plt.savefig(buf, format='png') plt.close() buf.seek(0) string = base64.b64encode(buf.read()).decode('utf-8') # Output pic return "data:image/png;base64,"+ string def on_instant1(event): try: window_size = int(Element("C2").element.value) forecast_periods = int(Element("D2").element.value) json_str = Element("wma_result").element.innerHTML if json_str != '': json_data = json.loads(json_str) df = pd.DataFrame(json_data) df['T'] = pd.to_numeric(df['T'], errors='coerce').astype('int64') df['Yt'] = pd.to_numeric(df['Yt'], errors='coerce').astype('float64') img_data = get_plot_string1(df, window_size, forecast_periods) Element("out_instant1").element.innerHTML = "Plt successed." Element("plot_image1").element.src = img_data Element("plot_image1").element.style.display = "block" else: Element("out_instant1").element.innerHTML = "Please upload csv file." except ValueError: Element("out_instant1").element.innerHTML = "Invalid input. Please enter numeric values." Element("button_instant1").element.onclick = on_instant1

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Regular time series - Constand Trend(EMA) Watch Tutorial

import json import pandas as pd def get_plot_string2(data, alpha): result = calculate_ema(data, alpha) plt = plot_data_ema(result, alpha) buf = io.BytesIO() plt.savefig(buf, format='png') plt.close() buf.seek(0) string = base64.b64encode(buf.read()).decode('utf-8') # output pic return "data:image/png;base64,"+ string def on_instant2(event): try: alpha = float(Element("C3").element.value) json_str = Element("ema_result").element.innerHTML if json_str != '': json_data = json.loads(json_str) df = pd.DataFrame(json_data) df['T'] = pd.to_numeric(df['T'], errors='coerce').astype('int64') df['Yt'] = pd.to_numeric(df['Yt'], errors='coerce').astype('float64') img_data = get_plot_string2(df, alpha) Element("out_instant2").element.innerHTML = "Plt successed." Element("plot_image2").element.src = img_data Element("plot_image2").element.style.display = "block" else: Element("out_instant2").element.innerHTML = "Please upload csv file." except ValueError: Element("out_instant2").element.innerHTML = "Invalid input. Please enter numeric values." Element("button_instant2").element.onclick = on_instant2

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Regular time series - Constand Trend(Regression) Watch Tutorial

import json import pandas as pd def get_plot_string3(data, predict_number): a_t, b_t, t, Y = calculate_linear_regression(data) start_t = int(t[-1]) # last t predictions = predict_linear(a_t, b_t, start_t, predict_number) plt = plot_regression_linear(t, Y, a_t, b_t, predictions) buf = io.BytesIO() plt.savefig(buf, format='png') plt.close() buf.seek(0) string = base64.b64encode(buf.read()).decode('utf-8') # output return "data:image/png;base64,"+ string def on_instant3(event): try: predict_number = int(Element("C4").element.value) json_str = Element("linear_result").element.innerHTML if json_str != '': json_data = json.loads(json_str) df = pd.DataFrame(json_data) df['T'] = pd.to_numeric(df['T'], errors='coerce').astype('int64') df['Yt'] = pd.to_numeric(df['Yt'], errors='coerce').astype('float64') img_data = get_plot_string3(df, predict_number) Element("out_instant3").element.innerHTML = "Plt successed." Element("plot_image3").element.src = img_data Element("plot_image3").element.style.display = "block" else: Element("out_instant3").element.innerHTML = "Please upload csv file." except ValueError: Element("out_instant3").element.innerHTML = "Invalid input. Please enter numeric values." Element("button_instant3").element.onclick = on_instant3

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Regular time series - Constand Trend(Holt) Watch Tutorial

import json import pandas as pd def get_plot_string4(data, alpha, beta, num_forecasts): Y = data['Yt'].values a, b, P = holt_linear_exponential_smoothing(Y, alpha, beta) last_a = a[-1] last_b = b[-1] # Forecast future values future_forecasts = forecast_future_values_holt( last_a, last_b, num_forecasts) plt = plot_results_holt(Y, a, P, future_forecasts, num_forecasts) buf = io.BytesIO() plt.savefig(buf, format='png') plt.close() buf.seek(0) string = base64.b64encode(buf.read()).decode('utf-8') # output return "data:image/png;base64,"+ string def on_instant2(event): try: alpha = float(Element("C5").element.value) beta = float(Element("D5").element.value) num_forecasts = int(Element("E5").element.value) if not (0 < alpha < 1): raise ValueError("Alpha must be between 0 and 1.") if not (0 < beta < 1): raise ValueError("Beta must be between 0 and 1.") json_str=Element("hot_result").element.innerHTML if json_str !='' : json_data=json.loads(json_str) df=pd.DataFrame(json_data) df['T']=pd.to_numeric(df['T'], errors='coerce' ).astype('int64') df['Yt']=pd.to_numeric(df['Yt'], errors='coerce' ).astype('float64') img_data=get_plot_string4(df, alpha, beta, num_forecasts) Element("out_instant4").element.innerHTML="Plt successed." Element("plot_image4").element.src=img_data Element("plot_image4").element.style.display="block" else: Element("out_instant4").element.innerHTML="Please upload csv file." except ValueError: Element("out_instant4").element.innerHTML="Invalid input. Please enter numeric values." Element("button_instant4").element.onclick=on_instant2

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Regular time series - Constand Trend(Winter) Watch Tutorial

import json import pandas as pd def get_plot_string5(data, alpha, beta, gamma, M, n_forecasts): Yt = data['Yt'] # Calculate A, B, S A, B, S = calculate_ABC(Yt, alpha, beta, gamma, M) group_size = len(Yt) // M forecasts = forecast_seasons(A, B, S, n_forecasts, group_size) # Graph part forecast_df = pd.DataFrame({ 'Time Period': range(len(Yt) + 1, len(Yt) + len(forecasts) + 1), 'Forecast': forecasts }) plt.figure(figsize=(12, 6)) plt.plot(range(1, len(Yt) + 1), Yt, label='Original Data') plt.plot( forecast_df['Time Period'], forecast_df['Forecast'], label='Forecasted Data', linestyle='--') plt.xlabel('Time Period') plt.ylabel('Values') plt.title('Seasonal Forecasting') plt.legend() buf = io.BytesIO() plt.savefig(buf, format='png') plt.close() buf.seek(0) string = base64.b64encode(buf.read()).decode('utf-8') # Output return "data:image/png;base64,"+ string def on_instant5(event): try: alpha = float(Element("C6").element.value) beta = float(Element("D6").element.value) gamma = float(Element("E6").element.value) M = int(Element("F6").element.value) n_forecasts = int(Element("G6").element.value) if not (0 < alpha < 1): raise ValueError("Alpha must be between 0 and 1.") if not (0 < beta < 1): raise ValueError("Beta must be between 0 and 1.") if not (0 < gamma < 1): raise ValueError("Gamma must be between 0 and 1.") json_str = Element("winter_result").element.innerHTML if json_str != '': json_data = json.loads(json_str) df = pd.DataFrame(json_data) df['T'] = pd.to_numeric(df['T'], errors='coerce').astype('int64') df['Yt'] = pd.to_numeric(df['Yt'], errors='coerce').astype('float64') img_data = get_plot_string5(df, alpha, beta, gamma, M, n_forecasts) Element("out_instant5").element.innerHTML = "Plt successed." Element("plot_image5").element.src = img_data Element("plot_image5").element.style.display = "block" else: Element("out_instant5").element.innerHTML = "Please upload csv file." except ValueError: Element("out_instant5").element.innerHTML = "Invalid input. Please enter numeric values." Element("button_instant5").element.onclick = on_instant5

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