├── acf and pacf.png
├── demand over time.jpg
├── inventory over time.jpg
├── time series forcast.png
├── LICENSE
├── CONTRIBUTING.md
├── README.md
└── demand inv.ipynb


/acf and pacf.png:
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https://raw.githubusercontent.com/DavieObi/Demand-Forecasting-and-Inventory-Optimization/HEAD/acf and pacf.png


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/demand over time.jpg:
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https://raw.githubusercontent.com/DavieObi/Demand-Forecasting-and-Inventory-Optimization/HEAD/demand over time.jpg


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/inventory over time.jpg:
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https://raw.githubusercontent.com/DavieObi/Demand-Forecasting-and-Inventory-Optimization/HEAD/inventory over time.jpg


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/time series forcast.png:
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https://raw.githubusercontent.com/DavieObi/Demand-Forecasting-and-Inventory-Optimization/HEAD/time series forcast.png


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/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2025 DavieObi
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.


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/CONTRIBUTING.md:
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 1 | # Contributing to Demand Forecasting and Inventory Optimization
 2 | 
 3 | Thank you for showing interest in contributing to **Demand Forecasting and Inventory Optimization**!  
 4 | This project thrives on community collaboration, and we welcome everyone — from first-time open-source contributors to experienced data scientists — to participate.
 5 | 
 6 | ---
 7 | 
 8 | ## 🏆 Ways You Can Contribute
 9 | 
10 | We value contributions of all kinds, including:
11 | 
12 | - **Bug Fixes:** Help us identify and resolve issues in the code or documentation.
13 | - **Feature Enhancements:** Add new forecasting techniques, inventory optimization strategies, or visualization methods.
14 | - **Documentation:** Improve clarity, add tutorials, or create example workflows for beginners.
15 | - **Testing:** Write tests or verify existing code to ensure reliability and accuracy.
16 | - **Discussions:** Suggest ideas, request features, or share insights that can shape the project’s future.
17 | 
18 | ---
19 | 
20 | ## 🚀 Getting Started
21 | 
22 | 1. **Fork** the repository to your own GitHub account.
23 | 2. **Clone** your fork locally:
24 |    ```bash
25 |    git clone https://github.com/<your-username>/Demand-Forecasting-and-Inventory-Optimization.git


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/README.md:
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 1 | # Demand Forecasting and Inventory Optimization
 2 | 
 3 | ## Project Overview
 4 | This project focuses on analyzing historical demand and inventory data for a product, building a time series model to forecast future demand, and then leveraging these forecasts to calculate key inventory management parameters such as **optimal order quantity**, **reorder point**, and **safety stock**.  
 5 | The goal is to optimize inventory levels to meet customer demand efficiently while minimizing costs.
 6 | 
 7 | ---
 8 | 
 9 | ## Problem Statement
10 | Businesses often face challenges in managing inventory effectively due to unpredictable demand fluctuations.  
11 | - **Overstocking** leads to increased holding costs and potential waste.  
12 | - **Understocking** results in lost sales, customer dissatisfaction, and stockout costs.  
13 | 
14 | The core problem is to accurately predict future demand and translate these predictions into actionable inventory control strategies to maintain optimal stock levels.
15 | 
16 | ---
17 | 
18 | ## Project Objective
19 | The primary objectives of this project are:
20 | 
21 | 1. **Analyze** historical demand and inventory trends to understand underlying patterns.
22 | 2. **Build** a robust time series forecasting model (e.g., ARIMA) for future demand.
23 | 3. **Utilize** the demand forecasts to calculate optimal inventory parameters, including:
24 |    - Reorder points
25 |    - Safety stock
26 |    - Order quantities  
27 |    aiming for a specified service level.
28 | 4. **Demonstrate** the application of time series analysis in practical inventory management scenarios.
29 | 
30 | ---
31 | 
32 | ## Column Dictionary
33 | | Column       | Description                                                                 |
34 | |--------------|-----------------------------------------------------------------------------|
35 | | **Date**     | The specific date for which the demand and inventory data are recorded. (e.g., YYYY-MM-DD) |
36 | | **Product_ID** | A unique identifier for the product being tracked. (e.g., P1)             |
37 | | **Demand**   | The quantity of the product demanded on that specific date. (Integer)       |
38 | | **Inventory**| The quantity of the product available in stock at the end of that date. (Integer) |
39 | 
40 | ---
41 | 
42 | 
43 | ### Insights from Data Analysis
44 | - **Demand Volatility:**  
45 |   Analysis of "Demand Over Time" revealed significant day-to-day fluctuations, indicating a highly volatile demand pattern with no clear overall trend over the observed period (June 2023 - January 2024).  
46 |   ➜ This underscores the need for a robust forecasting approach.
47 | 
48 | - **Decreasing Inventory:**  
49 |   The "Inventory Over Time" plot showed a consistent downward trend in inventory levels, suggesting demand was depleting stock faster than it was replenished.  
50 |   ➜ This highlighted a potential risk of stockouts if left unaddressed.
51 | 
52 | - **Time Series Properties (ACF/PACF):**  
53 |   The Autocorrelation Function (ACF) plot exhibited a slow decay, indicating **non-stationarity** in the demand time series.  
54 |   ➜ First-order differencing (d=1) was required to stabilize the series before applying ARIMA.
55 | 
56 | ---
57 | 
58 | ### Forecasting and Inventory Optimization
59 | 
60 | #### ARIMA Model
61 | - A non-seasonal **ARIMA(1,1,1)** model was implemented.  
62 | - The choice of **d=1** was validated by the ACF plot.  
63 | - For the simulated data used, the model produced a **flat forecast of 74 units/day for 10 days**.  
64 |   ➜ While useful for demonstration, real-world cases may require **SARIMA** or other advanced models to capture seasonality and complex patterns.
65 | 
66 | ---
67 | 
68 | ### Inventory Parameter Calculation
69 | Using the forecasted demand, the following inventory parameters were derived:
70 | 
71 | | Parameter             | Value  | Description |
72 | |-----------------------|--------|-------------|
73 | | **Optimal Order Quantity** | 148 units | Recommended order size when stock reaches the reorder point. |
74 | | **Reorder Point**         | 148 units | The inventory level at which a new order should be placed. |
75 | | **Safety Stock**          | 74 units  | Buffer stock to mitigate stockout risks due to demand spikes or lead time variability. |
76 | | **Total Cost**            | 557.4     | Driven mainly by holding costs due to high initial inventory and zero stockout costs. |
77 | 
78 | ---
79 | 
80 | ## Conclusion
81 | This project successfully demonstrates a pipeline for **demand forecasting** and its application in deriving **practical inventory management parameters**.  
82 | 
83 | - By understanding historical patterns, forecasting future demand, and applying inventory models, businesses can make **data-driven decisions** to:
84 |   - Optimize stock levels
85 |   - Minimize holding and stockout costs
86 |   - Improve customer service  
87 | 
88 | The flat ARIMA forecast (on simulated data) highlights the importance of selecting appropriate model parameters and potentially incorporating **seasonal components (SARIMA)** in real-world scenarios.  
89 | 
90 | ### ✅ **Future Work**
91 | - Refine forecasting models with more complex techniques.  
92 | - Incorporate advanced inventory optimization methods.  
93 | - Integrate real-time data for dynamic inventory adjustments.
94 | ```
95 | 


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/demand inv.ipynb:
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   5 |    "execution_count": 1,
   6 |    "id": "4bd2257c",
   7 |    "metadata": {},
   8 |    "outputs": [
   9 |     {
  10 |      "name": "stdout",
  11 |      "output_type": "stream",
  12 |      "text": [
  13 |       "   Unnamed: 0        Date Product_ID  Demand  Inventory\n",
  14 |       "0           0  2023-06-01         P1      51       5500\n",
  15 |       "1           1  2023-06-02         P1     141       5449\n",
  16 |       "2           2  2023-06-03         P1     172       5308\n",
  17 |       "3           3  2023-06-04         P1      91       5136\n",
  18 |       "4           4  2023-06-05         P1     198       5045\n"
  19 |      ]
  20 |     }
  21 |    ],
  22 |    "source": [
  23 |     "import pandas as pd\n",
  24 |     "import numpy as np\n",
  25 |     "import plotly.express as px\n",
  26 |     "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
  27 |     "import matplotlib.pyplot as plt\n",
  28 |     "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
  29 |     "\n",
  30 |     "data = pd.read_csv(\"demand_inventory.csv\")\n",
  31 |     "print(data.head())"
  32 |    ]
  33 |   },
  34 |   {
  35 |    "cell_type": "code",
  36 |    "execution_count": 2,
  37 |    "id": "171665ae",
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 984 |            },
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 986 |             "gridcolor": "white",
 987 |             "linecolor": "white",
 988 |             "ticks": ""
 989 |            },
 990 |            "bgcolor": "#E5ECF6",
 991 |            "caxis": {
 992 |             "gridcolor": "white",
 993 |             "linecolor": "white",
 994 |             "ticks": ""
 995 |            }
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 997 |           "title": {
 998 |            "x": 0.05
 999 |           },
1000 |           "xaxis": {
1001 |            "automargin": true,
1002 |            "gridcolor": "white",
1003 |            "linecolor": "white",
1004 |            "ticks": "",
1005 |            "title": {
1006 |             "standoff": 15
1007 |            },
1008 |            "zerolinecolor": "white",
1009 |            "zerolinewidth": 2
1010 |           },
1011 |           "yaxis": {
1012 |            "automargin": true,
1013 |            "gridcolor": "white",
1014 |            "linecolor": "white",
1015 |            "ticks": "",
1016 |            "title": {
1017 |             "standoff": 15
1018 |            },
1019 |            "zerolinecolor": "white",
1020 |            "zerolinewidth": 2
1021 |           }
1022 |          }
1023 |         },
1024 |         "title": {
1025 |          "text": "Demand Over Time"
1026 |         },
1027 |         "xaxis": {
1028 |          "anchor": "y",
1029 |          "domain": [
1030 |           0,
1031 |           1
1032 |          ],
1033 |          "title": {
1034 |           "text": "Date"
1035 |          }
1036 |         },
1037 |         "yaxis": {
1038 |          "anchor": "x",
1039 |          "domain": [
1040 |           0,
1041 |           1
1042 |          ],
1043 |          "title": {
1044 |           "text": "Demand"
1045 |          }
1046 |         }
1047 |        }
1048 |       }
1049 |      },
1050 |      "metadata": {},
1051 |      "output_type": "display_data"
1052 |     }
1053 |    ],
1054 |    "source": [
1055 |     "fig_demand = px.line(data, x='Date',\n",
1056 |     "                     y='Demand',\n",
1057 |     "                     title='Demand Over Time')\n",
1058 |     "fig_demand.show()"
1059 |    ]
1060 |   },
1061 |   {
1062 |    "cell_type": "code",
1063 |    "execution_count": 4,
1064 |    "id": "f1a260e5",
1065 |    "metadata": {},
1066 |    "outputs": [
1067 |     {
1068 |      "data": {
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1071 |         "plotlyServerURL": "https://plot.ly"
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1079 |           "dash": "solid"
1080 |          },
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1087 |          "showlegend": false,
1088 |          "type": "scatter",
1089 |          "x": [
1090 |           "2023-06-01",
1091 |           "2023-06-02",
1092 |           "2023-06-03",
1093 |           "2023-06-04",
1094 |           "2023-06-05",
1095 |           "2023-06-06",
1096 |           "2023-06-07",
1097 |           "2023-06-08",
1098 |           "2023-06-09",
1099 |           "2023-06-10",
1100 |           "2023-06-11",
1101 |           "2023-06-12",
1102 |           "2023-06-13",
1103 |           "2023-06-14",
1104 |           "2023-06-15",
1105 |           "2023-06-16",
1106 |           "2023-06-17",
1107 |           "2023-06-18",
1108 |           "2023-06-19",
1109 |           "2023-06-20",
1110 |           "2023-06-21",
1111 |           "2023-06-22",
1112 |           "2023-06-23",
1113 |           "2023-06-24",
1114 |           "2023-06-25",
1115 |           "2023-06-26",
1116 |           "2023-06-27",
1117 |           "2023-06-28",
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1119 |           "2023-06-30",
1120 |           "2023-07-01",
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1128 |           "2023-07-09",
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1917 |           "colorway": [
1918 |            "#636efa",
1919 |            "#EF553B",
1920 |            "#00cc96",
1921 |            "#ab63fa",
1922 |            "#FFA15A",
1923 |            "#19d3f3",
1924 |            "#FF6692",
1925 |            "#B6E880",
1926 |            "#FF97FF",
1927 |            "#FECB52"
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1929 |           "font": {
1930 |            "color": "#2a3f5f"
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1932 |           "geo": {
1933 |            "bgcolor": "white",
1934 |            "lakecolor": "white",
1935 |            "landcolor": "#E5ECF6",
1936 |            "showlakes": true,
1937 |            "showland": true,
1938 |            "subunitcolor": "white"
1939 |           },
1940 |           "hoverlabel": {
1941 |            "align": "left"
1942 |           },
1943 |           "hovermode": "closest",
1944 |           "mapbox": {
1945 |            "style": "light"
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1947 |           "paper_bgcolor": "white",
1948 |           "plot_bgcolor": "#E5ECF6",
1949 |           "polar": {
1950 |            "angularaxis": {
1951 |             "gridcolor": "white",
1952 |             "linecolor": "white",
1953 |             "ticks": ""
1954 |            },
1955 |            "bgcolor": "#E5ECF6",
1956 |            "radialaxis": {
1957 |             "gridcolor": "white",
1958 |             "linecolor": "white",
1959 |             "ticks": ""
1960 |            }
1961 |           },
1962 |           "scene": {
1963 |            "xaxis": {
1964 |             "backgroundcolor": "#E5ECF6",
1965 |             "gridcolor": "white",
1966 |             "gridwidth": 2,
1967 |             "linecolor": "white",
1968 |             "showbackground": true,
1969 |             "ticks": "",
1970 |             "zerolinecolor": "white"
1971 |            },
1972 |            "yaxis": {
1973 |             "backgroundcolor": "#E5ECF6",
1974 |             "gridcolor": "white",
1975 |             "gridwidth": 2,
1976 |             "linecolor": "white",
1977 |             "showbackground": true,
1978 |             "ticks": "",
1979 |             "zerolinecolor": "white"
1980 |            },
1981 |            "zaxis": {
1982 |             "backgroundcolor": "#E5ECF6",
1983 |             "gridcolor": "white",
1984 |             "gridwidth": 2,
1985 |             "linecolor": "white",
1986 |             "showbackground": true,
1987 |             "ticks": "",
1988 |             "zerolinecolor": "white"
1989 |            }
1990 |           },
1991 |           "shapedefaults": {
1992 |            "line": {
1993 |             "color": "#2a3f5f"
1994 |            }
1995 |           },
1996 |           "ternary": {
1997 |            "aaxis": {
1998 |             "gridcolor": "white",
1999 |             "linecolor": "white",
2000 |             "ticks": ""
2001 |            },
2002 |            "baxis": {
2003 |             "gridcolor": "white",
2004 |             "linecolor": "white",
2005 |             "ticks": ""
2006 |            },
2007 |            "bgcolor": "#E5ECF6",
2008 |            "caxis": {
2009 |             "gridcolor": "white",
2010 |             "linecolor": "white",
2011 |             "ticks": ""
2012 |            }
2013 |           },
2014 |           "title": {
2015 |            "x": 0.05
2016 |           },
2017 |           "xaxis": {
2018 |            "automargin": true,
2019 |            "gridcolor": "white",
2020 |            "linecolor": "white",
2021 |            "ticks": "",
2022 |            "title": {
2023 |             "standoff": 15
2024 |            },
2025 |            "zerolinecolor": "white",
2026 |            "zerolinewidth": 2
2027 |           },
2028 |           "yaxis": {
2029 |            "automargin": true,
2030 |            "gridcolor": "white",
2031 |            "linecolor": "white",
2032 |            "ticks": "",
2033 |            "title": {
2034 |             "standoff": 15
2035 |            },
2036 |            "zerolinecolor": "white",
2037 |            "zerolinewidth": 2
2038 |           }
2039 |          }
2040 |         },
2041 |         "title": {
2042 |          "text": "Inventory Over Time"
2043 |         },
2044 |         "xaxis": {
2045 |          "anchor": "y",
2046 |          "domain": [
2047 |           0,
2048 |           1
2049 |          ],
2050 |          "title": {
2051 |           "text": "Date"
2052 |          }
2053 |         },
2054 |         "yaxis": {
2055 |          "anchor": "x",
2056 |          "domain": [
2057 |           0,
2058 |           1
2059 |          ],
2060 |          "title": {
2061 |           "text": "Inventory"
2062 |          }
2063 |         }
2064 |        }
2065 |       }
2066 |      },
2067 |      "metadata": {},
2068 |      "output_type": "display_data"
2069 |     }
2070 |    ],
2071 |    "source": [
2072 |     "fig_inventory = px.line(data, x='Date',\n",
2073 |     "                        y='Inventory',\n",
2074 |     "                        title='Inventory Over Time')\n",
2075 |     "fig_inventory.show()"
2076 |    ]
2077 |   },
2078 |   {
2079 |    "cell_type": "code",
2080 |    "execution_count": 9,
2081 |    "id": "810ff52d",
2082 |    "metadata": {},
2083 |    "outputs": [
2084 |     {
2085 |      "name": "stdout",
2086 |      "output_type": "stream",
2087 |      "text": [
2088 |       "Number of observations in differenced_series_small: 4\n",
2089 |       "Number of observations in differenced_series_larger: 49\n"
2090 |      ]
2091 |     },
2092 |     {
2093 |      "data": {
2094 |       "image/png": 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hojpFV9UXOw46fY3sMjyZJ6/WgeLhKaueAAAAeCritdKNpBQuKje7/O668x/OpfPZZSOyy/BsufOpOevymzuf2m2ta7q5VcUnKSVda3Yd0ZFTGaoa7KdO0VUVYQ0o6WaVOXmHAPv7eJVgawAAgCchXisexGulF0kpFArZZZRHnjyf2ppdh/Xe+j22pJtF0hc7Dmp4hzpM9A8AAFAKEK+hPGBOKRQaE4KjvPHU+dQuHJaYY2T377vr9ig55WxJNxEAAKBcI15DeUFSCgDy4amrdeQOS3Qmd1giAAAASk55iNdY4RoSw/cAIF+eOp+aJw9LBAAA8ASeHq8xNBG5SEoBQAE8cT613GGJ+U3gXlaHJQIAAHgKT47XWOEaF2L4HgBchKfNp+apwxIBAAA8hSfHa+VhaCIKj6QUAJQzucMSLRdEAxUsksVStoclAgAAeApPjtc8fWgiiobhewCKRVJKutbsOqIjpzJUNdhPnaKrKsIaUNLNQj48cVgiAACAJ/HUeM2Thyai6EhKAbhsTFRYNuUdlujv4+XS45G4BAAAKBp3x2vu0Cm6qr7YcdDpa2V9aCKKzuXD995++21FRUXJ399fLVu21Pr16wssv3btWrVs2VL+/v6qU6eOpk2b5lBm/vz5atiwofz8/NSwYUMtXLjQVc0HcBEXTlSYY2T377vr9ig55axLjsnysWXLml2H9cin27Vkx0Ft3HNMS3Yc1COfbtfaX5gzAMgPMRQAwBN58tBEFJ1Lk1Lz5s3TqFGj9OSTTyoxMVHt27dXr169tH//fqfl9+7dq969e6t9+/ZKTEzUE088oZEjR2r+/Pm2MgkJCRowYIDi4+O1fft2xcfHq3///tq0aZMrTwVAPtw9USHJjbKnJBKXQFlHDAUA8GQd61XTpBub2J73bByul2+9mlEW5ZBLh++9/PLLGjJkiIYOHSpJevXVV/XVV1/pnXfe0aRJkxzKT5s2TTVr1tSrr74qSWrQoIG2bt2qF198UTfffLOtju7du2vs2LGSpLFjx2rt2rV69dVXNWfOnCK178y5LHmfy7qMM3Rep7P/F8bZzGydzcwu1vYUp4wL2pZRittZVJ56Xu5yKPVsgRMVHko9W2z3dXLq2QKXj61dJciui3Nx8dR7xF3ntfLnQwXOG7Di52Td2rKGy47vap56f7jb5VzH9MzsIv/NLQpX1p0fYqiix1AASpfk1LNav/uIjp06pyrBvmp/VVWFuyBO83SeHGdcEeBj+3+/ppHy8/Fy2e9zT72Ol3tepSF+shhj8vs8eVnOnTunwMBAffrpp7rxxhtt2x966CFt27ZNa9euddinQ4cOat68uV577TXbtoULF6p///46c+aMfHx8VLNmTT388MN6+OGHbWVeeeUVvfrqq/r999+dtiUjI0MZGX/P4J+amqoaNWqoxqhPVMEvsDhOFwAAeKicjDM68Gp/paSkKCQkxOXHI4YCAABlXWHjJ5cN3zt69Kiys7MVFhZmtz0sLEzJyclO90lOTnZaPisrS0ePHi2wTH51StKkSZNktVptjxo1yu438gAAwLMRQwEAgPLC5avvWSz2s80YYxy2Xax83u1FrXPs2LEaPXq07Xnut3ybn+zqlm88C+tsZra2H0gpdPmMzGzd+9H3kqRpt7eQnwesxJCLcytbx/pm91HN2LDXbvU9I+mfsVG69qrQYjvOp98d0PKdycpx0r+zguX8WPSyPAxM8sx7Pzn1rJ5Y+IOc9cu1WKRJNzYp9mGXnngdJfefV1m5jqEV/VS3apDL6k9NTVXEqy6rPl/EUIW3ac/xIpX3xL/FJXE8jlX2jueOY5VUvOaOcyuJmMadysrf/dKuLF3HNnUqu6zuwsZPLktKhYaGysvLy+Hbt8OHDzt8S5crPDzcaXlvb29VqVKlwDL51SlJfn5+8vPzc9ge6OutQF+X5+UKrYLFcslLfPr5eHnE8qDOcG6XLjn17wmkF+84qG4NwhRhDSj243RrGKbGV1q1etdhHTmVoarBfuocXa3YV87o1iBMy3Y6/0bfSOreINyj7hVPufdrVwnS8A519O66PQ6Jy+Ed6qhWFdclEyTPuY55ufu8SvN1DPDxcunf8yw3xwrEUEV3OfemO+9tT/655Vhl73iuOtaJM5kFzjd64kymy8/RVeeW8NuxAufJ3PDbMd3WumaxH7cklOa/+2VJab+OpSF+ctnwPV9fX7Vs2VIrVqyw275ixQrFxsY63ScmJsah/H//+1+1atVKPj4+BZbJr06gvFqz67CeWPiD7fnynckuXaUu3Oqv21rX1MguV+m21jVdspTrhcvH5i4by/KxZUPHetX08q1Xq2/TSLWtU0V9m0aywsoluDDR/Ol3B5SUkl6CrYGrEEMBKMuqBvsVuDJz1WDHRHdZceRURoEJtyOnMvJ5FUB+XPoV1+jRoxUfH69WrVopJiZG7733nvbv3697771X0vku4X/++afef/99SdK9996rN998U6NHj9awYcOUkJCg6dOn260I89BDD6lDhw6aMmWKrr/+en3++edauXKlvvnmG1eeClCmJKWk21apy5XbhfrddXsUHRZSZhM4HetVU3RYiMt7ZaH45SYucWnW7Dqs99bvsT1fvjNZy3Yma3iHOiT3PBAxFICyqlN0VX2x46DT14ykztGu+ZuV94sbV4wQyE245ddTqiwn3ICS4tKk1IABA3Ts2DFNnDhRSUlJaty4sZYuXapatWpJkpKSkrR//35b+aioKC1dulQPP/yw3nrrLUVGRur111+3LWUsSbGxsZo7d66eeuopPf3006pbt67mzZunNm3auPJUgDJlza4jBf7BXL3rcJlODpDcQHnjyYlmOEcMBaCsyu3Znt+wfVf8vXLXFzcllXADPJnLJwO4//77df/99zt9bdasWQ7bOnbsqO+//77AOm+55RbdcsstxdE8wCPRtRjwLJ6eaIZzxFAAyip39mx35xc3JZFwAzxd6ZmhEkCxoWsx4FlINAMo79wxNAvFy1092939xQ1TSQDFi6QU4IHoWgx4FhLNAMoz5tRDQUriixumkgCKj8tW3wPgnDtWz2KVOsCzdIquWmDATaIZgKfKb2iWMeeHZiWnnM1/Z5QLnrzaH1Ae0FMKcCN3ftNH12LAczCHBYDyijn1cDGMEADKNpJSgJuUxOpZdC1GeeWJc4+QaAZQHjGnHi6GL26Aso2kFOAmfNMHuIcnzz1CohlAaeKOLwCYUw+FwRc3QNlFUgpwE77pQ2F4Yg8fdyqJHokAUB656wsAhmahsPjiBiibmOgcpZI7JgN3NyZhxMWs2XVYTyz8wfZ8+c5kPfLpdq395XAJtqpsye2R6Exuj0QAwOVx5+TjLN4CuI8nfgZD6UdPKZQ6njr0hm/6UBB6+BQPeiQCgOu5e0oChmYBruepn8FQ+tFTCqWKJy/7yzd9KAg9fIoHPRIBwPVK4guA3KFZI7tcpdta1yRuAoqRJ38GQ+lHUgqliqd/MO9Yr5pevvVq9W0aqbZ1qqhv00i9fOvVfPsAevgUk07RVQu8jvRILBq68QNwhi8AAM/i6Z/BULoxfA+lSnn4YM4kjHCG1YWKB8tCFx+68QPID1MSAJ6lPHwGQ+lFUgqlCh/MUV4R4Bcf5h65fMxxBqAgfAEAeBY+g6EkkZRCqcIHc5RXBPjFix6Jl8fdkxgDKHv4AgDwHHwGQ0kiKYVShQ/mKM8I8FFa0I0fQGF46hcAeefT69YgTBHWAI85HpAXn8FQkkhKodThgznKM08N8FG20I0fQHnl7vn0mL8PpQWfwYoPieaiISmFUokP5gBQcujGD6A8cvd8eszfh9KGz2CXj0Rz0VUo6QYAAIDSJbcbv8UiVbDI7l+68QPwVLnz6TmTO59eWT6e5NiDIyklvdiPAZRX+SWajTmfaE5OOZv/zuUYPaUAAIADuvEDKG/cPZ+eu49HDw7AtVgo5tKQlAIAAE7RjR9AeeLu+fTceTyGCgKux0Ixl4bhewAAAADKvU7RVQv8QFnc8+m583glMVQQKG9yE83OsFBM/khKAQAAACj33D2fnjuPRw8OwPXcndj2FAzfAwAAAAC5fz49dx3P3UMTgfIoN9H87ro9tp+33H9ZKCZ/JKUAAAAA4C/unk/PHcfrFF1VX+w46PQ1enAAxYeFYoqOpBQAAAAAeDB6cADuw0IxRUNSCgAAAAA8HD04AJRGLp3o/MSJE4qPj5fVapXValV8fLxOnjyZb/nMzEyNGTNGTZo0UVBQkCIjI3XnnXfq4EH7rqadOnWSxWKxewwcONCVpwIAAOAWxE8AXCW3B8fILlfpttY1SUgBKHEuTUoNGjRI27Zt0/Lly7V8+XJt27ZN8fHx+ZY/c+aMvv/+ez399NP6/vvvtWDBAv3yyy/q16+fQ9lhw4YpKSnJ9nj33XddeSoAAABuQfwEAADKC5cN3/v555+1fPlybdy4UW3atJEk/fvf/1ZMTIx27dql6Ohoh32sVqtWrFhht+2NN95Q69attX//ftWs+fe4zMDAQIWHh7uq+QAAAG5H/AQAAMoTl/WUSkhIkNVqtQVUktS2bVtZrVZt2LCh0PWkpKTIYrHoiiuusNv+0UcfKTQ0VI0aNdKjjz6qtLS0fOvIyMhQamqq3QMAAKC0KU3xk0QMBQAAXMtlPaWSk5NVrZrj0qLVqlVTcnJyoeo4e/asHn/8cQ0aNEghISG27bfffruioqIUHh6unTt3auzYsdq+fbvDt4S5Jk2apAkTJlzaiQAAALhJaYqfJGIoAADgWkXuKTV+/HiHSTLzPrZu3SpJslgsDvsbY5xuzyszM1MDBw5UTk6O3n77bbvXhg0bpm7duqlx48YaOHCgPvvsM61cuVLff/+907rGjh2rlJQU2+PAgQNFPW0AAIBLVhbjJ4kYqjgkp561/f/T7w4oKSW9BFsDAEDpUuSeUiNGjLjoSi21a9fWjh07dOjQIYfXjhw5orCwsAL3z8zMVP/+/bV3715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2095 |       "text/plain": [
2096 |        "<Figure size 1200x400 with 2 Axes>"
2097 |       ]
2098 |      },
2099 |      "metadata": {},
2100 |      "output_type": "display_data"
2101 |     }
2102 |    ],
2103 |    "source": [
2104 |     "import pandas as pd\n",
2105 |     "import matplotlib.pyplot as plt\n",
2106 |     "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
2107 |     "import numpy as np # Import numpy for creating longer series\n",
2108 |     "\n",
2109 |     "# --- Your original code (with corrected date format from previous issue) ---\n",
2110 |     "# Assuming 'data' DataFrame is already loaded and contains 'Date' and 'Demand' columns\n",
2111 |     "# Let's create a *very small* sample DataFrame to reproduce the error:\n",
2112 |     "# This DataFrame only has 5 data points\n",
2113 |     "data_small = pd.DataFrame({\n",
2114 |     "    'Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'],\n",
2115 |     "    'Demand': [10, 12, 15, 13, 16]\n",
2116 |     "})\n",
2117 |     "\n",
2118 |     "data_small['Date'] = pd.to_datetime(data_small['Date'], format='%Y-%m-%d')\n",
2119 |     "time_series_small = data_small.set_index('Date')['Demand']\n",
2120 |     "\n",
2121 |     "differenced_series_small = time_series_small.diff().dropna()\n",
2122 |     "print(f\"Number of observations in differenced_series_small: {len(differenced_series_small)}\")\n",
2123 |     "# For data_small: len(differenced_series_small) will be 4.\n",
2124 |     "# 4 // 2 - 1 = 2 - 1 = 1.\n",
2125 |     "# The default nlags calculation will try to be larger than 1, causing the error.\n",
2126 |     "\n",
2127 |     "\n",
2128 |     "# --- How to fix: Use more data! ---\n",
2129 |     "# Create a larger sample DataFrame (e.g., 50 data points)\n",
2130 |     "dates = pd.date_range(start='2023-01-01', periods=50, freq='D')\n",
2131 |     "demand = np.random.randint(50, 100, size=50) + np.arange(50) * 0.5 # Adding a trend for illustration\n",
2132 |     "data_larger = pd.DataFrame({\n",
2133 |     "    'Date': dates,\n",
2134 |     "    'Demand': demand\n",
2135 |     "})\n",
2136 |     "\n",
2137 |     "data_larger['Date'] = pd.to_datetime(data_larger['Date'], format='%Y-%m-%d') # Format should be correct now\n",
2138 |     "time_series_larger = data_larger.set_index('Date')['Demand']\n",
2139 |     "\n",
2140 |     "differenced_series_larger = time_series_larger.diff().dropna()\n",
2141 |     "print(f\"Number of observations in differenced_series_larger: {len(differenced_series_larger)}\")\n",
2142 |     "# For data_larger: len(differenced_series_larger) will be 49.\n",
2143 |     "# 49 // 2 - 1 = 24 - 1 = 23. Default nlags will be fine.\n",
2144 |     "\n",
2145 |     "\n",
2146 |     "# Plot ACF and PACF of differenced time series with sufficient data\n",
2147 |     "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
2148 |     "plot_acf(differenced_series_larger, ax=axes[0])\n",
2149 |     "plot_pacf(differenced_series_larger, ax=axes[1])\n",
2150 |     "plt.suptitle('ACF and PACF of Differenced Time Series (with sufficient data)', y=1.02)\n",
2151 |     "plt.tight_layout()\n",
2152 |     "plt.show()"
2153 |    ]
2154 |   },
2155 |   {
2156 |    "cell_type": "code",
2157 |    "execution_count": 10,
2158 |    "id": "8d479fe3",
2159 |    "metadata": {},
2160 |    "outputs": [
2161 |     {
2162 |      "name": "stdout",
2163 |      "output_type": "stream",
2164 |      "text": [
2165 |       "2023-03-02    74\n",
2166 |       "2023-03-03    74\n",
2167 |       "2023-03-04    74\n",
2168 |       "2023-03-05    74\n",
2169 |       "2023-03-06    74\n",
2170 |       "2023-03-07    74\n",
2171 |       "2023-03-08    74\n",
2172 |       "2023-03-09    74\n",
2173 |       "2023-03-10    74\n",
2174 |       "2023-03-11    74\n",
2175 |       "Freq: D, Name: predicted_mean, dtype: int32\n"
2176 |      ]
2177 |     },
2178 |     {
2179 |      "data": {
2180 |       "image/png": 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",
2181 |       "text/plain": [
2182 |        "<Figure size 1000x600 with 1 Axes>"
2183 |       ]
2184 |      },
2185 |      "metadata": {},
2186 |      "output_type": "display_data"
2187 |     }
2188 |    ],
2189 |    "source": [
2190 |     "import pandas as pd\n",
2191 |     "import numpy as np\n",
2192 |     "import matplotlib.pyplot as plt\n",
2193 |     "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
2194 |     "\n",
2195 |     "# Assuming 'time_series' is already created and represents your 2 months of data\n",
2196 |     "# For demonstration, let's create a time_series that spans exactly 2 months\n",
2197 |     "dates = pd.date_range(start='2023-01-01', periods=60, freq='D') # 60 days for 2 months\n",
2198 |     "demand = np.random.randint(50, 100, size=60) + np.sin(np.arange(60) * 0.5) * 10\n",
2199 |     "time_series = pd.Series(demand, index=dates, name='Demand')\n",
2200 |     "\n",
2201 |     "# 1. Remove the seasonal_order component\n",
2202 |     "order = (1, 1, 1) # Non-seasonal ARIMA (p, d, q)\n",
2203 |     "# seasonal_order is removed or set to (0, 0, 0, 0)\n",
2204 |     "# model = SARIMAX(time_series, order=order, seasonal_order=(0, 0, 0, 0)) # or simply omit seasonal_order\n",
2205 |     "\n",
2206 |     "model = SARIMAX(time_series, order=order) # This is equivalent to seasonal_order=(0,0,0,0)\n",
2207 |     "model_fit = model.fit(disp=False)\n",
2208 |     "\n",
2209 |     "future_steps = 10\n",
2210 |     "predictions = model_fit.predict(len(time_series), len(time_series) + future_steps - 1)\n",
2211 |     "predictions = predictions.astype(int)\n",
2212 |     "print(predictions)\n",
2213 |     "\n",
2214 |     "# Optional: Plotting for visualization\n",
2215 |     "plt.figure(figsize=(10, 6))\n",
2216 |     "plt.plot(time_series.index, time_series, label='Historical Data')\n",
2217 |     "forecast_index = pd.date_range(start=time_series.index[-1], periods=future_steps + 1, freq='D')[1:]\n",
2218 |     "plt.plot(forecast_index, predictions, label='Forecast', linestyle='--')\n",
2219 |     "plt.title('Time Series Forecast (Non-Seasonal ARIMA)')\n",
2220 |     "plt.xlabel('Date')\n",
2221 |     "plt.ylabel('Demand')\n",
2222 |     "plt.legend()\n",
2223 |     "plt.grid(True)\n",
2224 |     "plt.show()"
2225 |    ]
2226 |   },
2227 |   {
2228 |    "cell_type": "code",
2229 |    "execution_count": 11,
2230 |    "id": "c6eddecc",
2231 |    "metadata": {},
2232 |    "outputs": [
2233 |     {
2234 |      "name": "stdout",
2235 |      "output_type": "stream",
2236 |      "text": [
2237 |       "Optimal Order Quantity: 148\n",
2238 |       "Reorder Point: 148.0\n",
2239 |       "Safety Stock: 74.0\n",
2240 |       "Total Cost: 557.4\n"
2241 |      ]
2242 |     }
2243 |    ],
2244 |    "source": [
2245 |     "# Create date indices for the future predictions\n",
2246 |     "future_dates = pd.date_range(start=time_series.index[-1] + pd.DateOffset(days=1), periods=future_steps, freq='D')\n",
2247 |     "\n",
2248 |     "# Create a pandas Series with the predicted values and date indices\n",
2249 |     "forecasted_demand = pd.Series(predictions, index=future_dates)\n",
2250 |     "\n",
2251 |     "# Initial inventory level\n",
2252 |     "initial_inventory = 5500\n",
2253 |     "\n",
2254 |     "# Lead time (number of days it takes to replenish inventory) \n",
2255 |     "lead_time = 1 # it's different for every business, 1 is an example\n",
2256 |     "\n",
2257 |     "# Service level (probability of not stocking out)\n",
2258 |     "service_level = 0.95 # it's different for every business, 0.95 is an example\n",
2259 |     "\n",
2260 |     "# Calculate the optimal order quantity using the Newsvendor formula\n",
2261 |     "z = np.abs(np.percentile(forecasted_demand, 100 * (1 - service_level)))\n",
2262 |     "order_quantity = np.ceil(forecasted_demand.mean() + z).astype(int)\n",
2263 |     "\n",
2264 |     "# Calculate the reorder point\n",
2265 |     "reorder_point = forecasted_demand.mean() * lead_time + z\n",
2266 |     "\n",
2267 |     "# Calculate the optimal safety stock\n",
2268 |     "safety_stock = reorder_point - forecasted_demand.mean() * lead_time\n",
2269 |     "\n",
2270 |     "# Calculate the total cost (holding cost + stockout cost)\n",
2271 |     "holding_cost = 0.1  # it's different for every business, 0.1 is an example\n",
2272 |     "stockout_cost = 10  # # it's different for every business, 10 is an example\n",
2273 |     "total_holding_cost = holding_cost * (initial_inventory + 0.5 * order_quantity)\n",
2274 |     "total_stockout_cost = stockout_cost * np.maximum(0, forecasted_demand.mean() * lead_time - initial_inventory)\n",
2275 |     "\n",
2276 |     "# Calculate the total cost\n",
2277 |     "total_cost = total_holding_cost + total_stockout_cost\n",
2278 |     "\n",
2279 |     "print(\"Optimal Order Quantity:\", order_quantity)\n",
2280 |     "print(\"Reorder Point:\", reorder_point)\n",
2281 |     "print(\"Safety Stock:\", safety_stock)\n",
2282 |     "print(\"Total Cost:\", total_cost)"
2283 |    ]
2284 |   },
2285 |   {
2286 |    "cell_type": "code",
2287 |    "execution_count": null,
2288 |    "id": "602b3713",
2289 |    "metadata": {},
2290 |    "outputs": [],
2291 |    "source": []
2292 |   }
2293 |  ],
2294 |  "metadata": {
2295 |   "kernelspec": {
2296 |    "display_name": "base",
2297 |    "language": "python",
2298 |    "name": "python3"
2299 |   },
2300 |   "language_info": {
2301 |    "codemirror_mode": {
2302 |     "name": "ipython",
2303 |     "version": 3
2304 |    },
2305 |    "file_extension": ".py",
2306 |    "mimetype": "text/x-python",
2307 |    "name": "python",
2308 |    "nbconvert_exporter": "python",
2309 |    "pygments_lexer": "ipython3",
2310 |    "version": "3.11.7"
2311 |   }
2312 |  },
2313 |  "nbformat": 4,
2314 |  "nbformat_minor": 5
2315 | }
2316 | 


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