1 Introduction (153.39 MB)
10 Causal Methods (13.8 MB)
11 Stationarity of the data (18.6 MB)
12 Summary (157.38 MB)
2 Forecasting is the stepping stone of planning (11.99 MB)
3 Time Series (17.06 MB)
4 Difficulties in forecasting (13.49 MB)
5 Forecasting applications (19.72 MB)
6 Forecasting in inventory management (20.46 MB)
7 Different Forecasting Methods (14.32 MB)
8 2020 and COVID (8.65 MB)
9 Time Series analysis (22.04 MB)
1 Introduction (40.73 MB)
10 Arima in python (109.99 MB)
11 ARIMA diagnostics (19.24 MB)
12 Grid search (52.59 MB)
13 For looping ARIMA (110.54 MB)
14 error handling (80.89 MB)
15 fitting the best model (31.82 MB)
16 Mean absolute error (41.48 MB)
17 Arima Comparison (39.42 MB)
18 Exponential smoothing (21.25 MB)
19 Exponential smoothing in python (90.75 MB)
2 Time Series Intro (141.08 MB)
20 Comparing exponential smoothing models (83.93 MB)
21 Time series summary (69.97 MB)
22 Assignment (18.3 MB)
22 1 retail clean csv (14.67 MB)
23 Assignment Explanation 1 (44.12 MB)
24 assignment explanation 2 (42.44 MB)
25 Assignment explanation 3 (37.2 MB)
26 Assignment Explanation 4 (27.21 MB)
3 Accuracy Measures (7.54 MB)
4 Preparing the data for time-series (76.86 MB)
5 Getting the time series components Lecture (9.52 MB)
6 Getting the time series components (11.99 MB)
7 components uses (36.8 MB)
8 Arima Models (46.23 MB)
9 Stationarity test in python (54.26 MB)
1 Installing sktime (101.43 MB)
10 Deriving the future (46.79 MB)
11 updating the time series with extra 2 years (81.47 MB)
12 Defining a forecast function (36.73 MB)
13 Transformed target Regressor (62.12 MB)
14 Testing the function (60.14 MB)
15 Plotting the results (59.16 MB)
16 Measuring acccuracy (61.62 MB)
17 Cross Validation (83.92 MB)
18 Conclusion (20.18 MB)
19 Assignment (32.61 MB)
2 Why Forecasting is different from normal machine learning sklearn (42.72 MB)
20 Assignment Explanation part 1 (27.04 MB)
21 assignment explanation part 2 (18.17 MB)
22 Assignment explanation part 3 (27.45 MB)
23 Assignment part 4 (67.69 MB)
24 Assignment part 5 (55.26 MB)
25 Assignment Part 6 (75.43 MB)
26 Assignment last part (67.03 MB)
27 Summary (61.54 MB)
3 Different Fitting strategies with sktime (29.83 MB)
4 Different estimators in sktime (41.81 MB)
5 Libraries (53.99 MB)
6 Transforming from weekly to monthly timeseries (32.87 MB)
7 Changing from a normal date to a period date (31.41 MB)
8 Splitting timeseries (31.42 MB)
9 Knearestneighbor (77.26 MB)
1 Introduction (299.31 MB)
10 Fitting Multiple models at once (27.08 MB)
11 Aggregations (36.93 MB)
12 Bottom up Forecasting (32.68 MB)
13 Top Down forecasting (49.48 MB)
14 Comparing Forecasts (52.78 MB)
15 Level 0 Comparison (24.37 MB)
16 Level 0 part 2 (45.04 MB)
17 Topdown and weighted least squares (22.24 MB)
18 Final note (17.81 MB)
2 Levels of a Hierarchy (17.87 MB)
3 Middle-out approach (13.78 MB)
4 Top Down approach (16.98 MB)
5 Forecasting level Usage (11.05 MB)
6 Reconciliation (31.89 MB)
7 Tourism Data (29.38 MB)
8 Making Quarterly series (28.63 MB)
9 Indexing as a Hierarchy (25.96 MB)
1 Introduction (110.73 MB)
2 Univariate Statistical analysis (78.8 MB)
3 Univariate Part2 (107.01 MB)
4 Bivariate Statistics (142.1 MB)
5 Auto-Correlation (60.35 MB)
6 Assignment (19.94 MB)
7 Assignment Solution (53.5 MB)
8 Summary (19.75 MB)
1 Simple Forecasting methods (176.13 MB)
10 Optimizing the Parameters (103.76 MB)
11 Best Simple Forecasting Method (77.24 MB)
12 Simple Forecasting assignments (23.03 MB)
13 Solution (51.12 MB)
14 Summary (50.78 MB)
2 Naive and Seasonal Naive (94.42 MB)
3 Mean Percentage error (52.83 MB)
4 Seasonal average (82.88 MB)
5 Mean absolute scaled error (41.79 MB)
6 Simple exponential smoothing and log transformations (93.63 MB)
7 Simple forecasting Methods (9.46 MB)
8 Naive and Simple forecasting methods (61.77 MB)
9 linear Regression , Custom weighted moving average and SES (102.26 MB)
1 Introduction (88.87 MB)
2 Moving Averages (26.93 MB)
3 De-trending series (49.5 MB)
4 Time-series Decomposition (16.63 MB)
5 Additive Decomposition (75.77 MB)
6 Multiplicative Decomposition (71.34 MB)
7 Assignment (16.29 MB)
8 Decomposition Solved (61.44 MB)
9 Summary (26.62 MB)
1 Introduction (73.79 MB)
10 12 Month ahead with multiplicative exponential smoothing (65.53 MB)
11 Assignment Holt (22.53 MB)
12 Assignment Solution (95.77 MB)
2 Simple Exponential Smoothing (13.52 MB)
3 Holt Exponential Smoothing (33.63 MB)
4 Initialization of alpha and Beta (35.97 MB)
5 Holt Model in Excel (95.77 MB)
6 Holt-winters Explanation (13.07 MB)
7 Additive Holt Winters Model (128.47 MB)
8 12 month Forecast with Holt Winters (62.77 MB)
9 Multiplicative Holt-Winters (64.53 MB)
1 introduction (82.24 MB)
2 Intro to linear regression (251.48 MB)
3 Multiple linear regression in excel (102.47 MB)
4 Fitting the model (97.61 MB)
5 Shifting to Python (83.58 MB)
1 Python! (58.42 MB)
2 downloading Anaconda (19.24 MB)
3 Installing Anaconda (16.13 MB)
4 Spyder overview (125.99 MB)
5 Jupiter Notebook overview (13.51 MB)
6 Python Libraries (33.72 MB)
7 Summary (26.24 MB)
1 Intro (164.54 MB)
10 Writing functions (34.8 MB)
11 mapping (9.72 MB)
12 for loops (9.76 MB)
13 for looping a function (17.59 MB)
14 Mapping On a data frame (31.69 MB)
15 for looping on a data frame (56.74 MB)
16 Summary (189.86 MB)
17 Assignment (11.88 MB)
18 Assignment answer 1 (58.66 MB)
19 Assignment answer 2 (106.32 MB)
2 Dataframes (56.83 MB)
2 1 Section 4 ipynb (7.43 KB)
3 Arithmetic Calculations with Python (32.44 MB)
3 1 online retail2 csv (14.66 MB)
4 Lists (28.46 MB)
5 Dictionaries (25.56 MB)
6 Arrays (15.5 MB)
7 Importing data in Python (56.82 MB)
8 Subsetting Data Frames (42.35 MB)
9 Conditions (21.81 MB)
1 Dates intro (144.77 MB)
10 rolling Time series 2 (33.48 MB)
11 Summary (103 MB)
12 Assignment (8.25 MB)
12 3 twentyeleven csv (7.96 MB)
13 Assignment answer (99.59 MB)
2 datetime (78.37 MB)
3 Last purchase date and recency (96.78 MB)
4 recency histogram (19.45 MB)
5 Modeling inter-arrival time (66.6 MB)
6 Modeling inter-arrival time 2 (53.08 MB)
7 Modeling inter-arrival time 3 (44.47 MB)
8 Resampling (104.67 MB)
9 rolling time series (25.84 MB)