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Complete Time Series Forecasting Bootcamp in Python (2025)
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Course content
Sections:
9
•
Activities:
1
•
Resources:
85
- Announcements
- 001 Welcome gJjh gitir
- 002 Defining time series sjII gitir
- 003 Baseline models J4oO gitir
- 004 Code Baseline models pfE7 gitir
- 005 Introducing the random walk 4HGk gitir
- 006 Code Simulate a random walk jLYZ gitir
- 007 Stationarity and differencing QDG7 gitir
- 008 Code Stationarity and differencing vfXl gitir
- 009 Autocorrelation UloD gitir
- 010 Code Autocorrelation DXWW gitir
- 011 Forecasting a random walk luOZ gitir
- 012 Code Forecasting a random walk ELus gitir
- 013 The moving average model c0UQ gitir
- 014 Code Forecasting with MA q FBAA gitir
- 015 The autoregressive model w7hS gitir
- 016 Code Forecasting with AR p Xnlp gitir
- 017 The ARMA model yOMd gitir
- 018 Designing a general modeling procedure EWBk gitir
- 019 Code Forecasting with ARMA p q Vqv3 gitir
- 020 The ARIMA model DhRP gitir
- 021 Code Forecasting with ARIMA p d q OqS9 gitir
- 022 Modeling seasonality 5s7o gitir
- 023 Code Forecasting with SARIMA d42s gitir
- 024 Adding external variables to our model sqZx gitir
- 025 Code Forecasting with SARIMAX lbwP gitir
- 026 Multivariate forecasting n8bg gitir
- 027 Code Forecasting with VAR xy1f gitir
- 028 Code Forecasting with VARMA AMbi gitir
- 029 Code Forecasting with VARMAX ojRU gitir
- 030 Simple exponential smoothing 4krw gitir
- 031 Code Forecasting with simple exponential smoothing 0Vym gitir
- 032 Double exponential smoothing mO7v gitir
- 033 Code Forecasting with double exponential smoothing XYXX gitir
- 034 Triple exponential smoothing hniI gitir
- 035 Code Forecasting with triple exponential smoothing vVH4 gitir
- 036 BATS and TBATS Fybs gitir
- 037 Code Forecasting with BATS and TBATS QBtq gitir
- 038 The Theta model wJVP gitir
- 039 Code Forecasting with the Theta model x5tV gitir
- 040 Code Comparing Theta to SARIMA khCP gitir
- 041 Introducing deep learning for time series forecasting 7Gjr gitir
- 042 Code Preprocessing data for deep learning xMgt gitir
- 043 Linear models qRxF gitir
- 044 Code Linear models aBl2 gitir
- 045 Deep neural networks OBGz gitir
- 046 Code Deep neural networks phiD gitir
- 047 LSTM vUyK gitir
- 048 Code LSTM AdZp gitir
- 049 Code CNN 2v15 gitir
- 050 CNN o2wi gitir
- 051 Understanding Prophet 5WVT gitir
- 052 Code Get started with Prophet ExiH gitir
- 053 Advanced features of Prophet KjCE gitir
- 054 Code Advanced features of Prophet Orc4 gitir
- 055 Hyperparameter tuning with Prophet GX0H gitir
- 056 Code Hyperparameter tuning with Prophet Xs2A gitir
- 057 Code Forecasing with Prophet G0m0 gitir
- 058 N BEATS vp7z gitir
- 059 Code NBEATS IgbZ gitir
- 060 NHITS CI9a gitir
- 061 Code NHITS mKM1 gitir
- 062 PatchTST rYY0 gitir
- 063 Code PatchTST RtNq gitir
- 064 TimesNet jvE3 gitir
- 065 Code TimesNet McX2 gitir
- 066 TiDE CXHo gitir
- 067 Code TiDE niSB gitir
- 068 TSMixer hxbl gitir
- 069 Code TSMixer J9ZJ gitir
- 070 iTransformer Ge7J gitir
- 071 Code iTransformer FutV gitir
- 072 SOFTS cUUT gitir
- 073 Code SOFTS J8ME gitir
- 074 RMoK X995 gitir
- 075 Code RMoK Hyi0 gitir
- 076 Introduction to intermittent time series forecasting XWsa gitir
- 077 Croston s method gH3Z gitir
- 078 Code Croston s method qUMd gitir
- 079 ADIDA and IMAPA 1mmS gitir
- 080 Code ADIDA and IMAPA ylcQ gitir
- 081 TSB rbV7 gitir
- 082 Code TSB 2qCX gitir
- 083 Error metrics for intermittent time series forecasting ltwD gitir
- 084 Code Error metrics for intermittent time series forecasting maZk gitir
- 085 Code Forecast the monthly sales of car parts wL8M gitir