Intelligent Inventory Management: Comparing Prophet and LSTM for Time‑Series Forecasting

Background : FuLu Network provides various recharge services (telecom, gaming, lifestyle) and faces challenges in managing inventory turnover and capital efficiency. To address this, an intelligent inventory management service is proposed that predicts daily consumption of each product based on order and product data.

Algorithm Selection : Two mature time‑series forecasting methods are recommended: Facebook's open‑source Prophet algorithm and an LSTM deep‑learning model. Their key characteristics are compared, highlighting training time, ease of use, data requirements, and multi‑dimensional capability.

Data Sources : The model uses (1) order data and (2) product classification data. The data format is a CSV with columns time,product,cnt where time is hourly, product is the product or category name, and cnt is the number of successful orders.

Feature Processing : Generally, time‑series data is used without additional features, though optional normalization or log‑transformation can be applied.

Prophet Details : Prophet is a commercial‑oriented statistical model that handles trend, seasonality, and holidays. The tuning steps include outlier removal, trend model selection (linear or logistic), changepoint specification, seasonal component configuration, and holiday effects. Grid‑search is recommended for hyper‑parameter optimization.

Prophet Code Example : import pandas as pd import numpy as np import matplotlib.pyplot as plt from fbprophet import Prophet data = pd.read_csv('../data/data2.csv', parse_dates=['time'], index_col='time') def get_product_data(name, rule=None): product = data[data['product'] == name][['cnt']] if rule is not None: product = product.resample(rule).sum() product.reset_index(inplace=True) product.columns = ['ds', 'y'] return product holidays = pd.read_csv('holiday.csv', parse_dates=['ds']) holidays['lower_window'] = -1 holidays = holidays.append(pd.DataFrame({ 'holiday': ['双11', '双12'], 'ds': pd.to_datetime(['2019-11-11', '2020-11-11', '2019-12-12', '2020-12-12']), 'lower_window': -1, 'upper_window': 1 })) m = Prophet(holidays=holidays) # ... fit and predict ...

LSTM Details : LSTM (Long Short‑Term Memory) networks are RNN variants designed for sequential data, featuring input, forget, and output gates. They can model multi‑dimensional time series and are suitable when large amounts of data are available.

LSTM Code Example : import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch from torch import nn from sklearn.preprocessing import MinMaxScaler ts_data = pd.read_csv('../data/data2.csv', parse_dates=['time'], index_col='time') def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): # Convert series to supervised learning format df = pd.DataFrame(data) cols, names = [], [] for i in range(n_in, 0, -1): cols.append(df.shift(i)) names += [f'var{j+1}(t-{i})' for j in range(df.shape[1])] for i in range(0, n_out): cols.append(df.shift(-i)) if i == 0: names += [f'var{j+1}(t)' for j in range(df.shape[1])] else: names += [f'var{j+1}(t+{i})' for j in range(df.shape[1])] agg = pd.concat(cols, axis=1) agg.columns = names if dropnan: agg.dropna(inplace=True) return agg # Transform data for LSTM scaler = MinMaxScaler((0,1)) yd = ts_data[ts_data['product']=='移动话费'][['cnt']] yd_scaled = scaler.fit_transform(yd.values) yd_supervised = series_to_supervised(yd_scaled, n_in=2).values.astype('float32') # Split, reshape, convert to torch tensors, define model, train, etc. class lstm_reg(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layers=2): super(lstm_reg, self).__init__() self.rnn = nn.LSTM(input_size, hidden_size, num_layers) self.reg = nn.Linear(hidden_size, output_size) def forward(self, x): x, _ = self.rnn(x) s, b, h = x.shape x = x.view(s*b, h) x = self.reg(x) x = x.view(s, b, -1) return x # ... training loop ...

Comparison : Prophet trains in seconds, requires no feature engineering, and works well with limited data; LSTM needs hours of training, extensive feature preprocessing, and large datasets but can capture multi‑dimensional relationships. The article provides quantitative error metrics (mean, max, min, 0.9‑quantile, etc.) and categorizes product‑algorithm fit into three types based on historical error thresholds.

Conclusion : For most inventory items with sparse data, Prophet offers a fast, reliable solution, while LSTM may be justified for high‑volume products where richer features and longer training are acceptable. References to Prophet documentation, papers, and LSTM tutorials are included.