Time-series Forecasting

Prophet is a forecasting model based on an additive/multiplicative model, where non-linear trends are fitted with seasonality. Well-suited for time series with strong seasonal patterns and multiple seasons of historical data. Prophet is highly adaptable to missing data, trend shifts, and outliers.

Croston is designed for intermittent demand forecasting, ideal for time series with missing values and sporadic demand patterns.

Croston TSB (Teunter-Syntetos-Babai) is an enhanced Croston method incorporating a smoothing parameter for both the demand interval and size, improving accuracy and responsiveness to fluctuations in demand.

Automated Machine Learning (AutoML ) techniques are integrated within the Time-series Forecasting to further automate and optimize the forecasting process.

You can define these hyperparameters:

• Uncertainty Samples
  • Affects the uncertainty forecast interval. Setting this to 0,0 reduces the number of output columns. Generates uncertainty intervals, upper and lower bands. Ensure the value is a float.
• Interval Width
  • The width of the uncertainty intervals. This hyperparameter determines the confidence interval for the forecast. For example, 0.8 stands for 80% confidence interval. Ensure the value is a float.
• Growth
  • Type of growth or trend in the time series. Can be set to linear, flat, or logistic. The auto setting uses Kendall's tau method to detect the trend type from the data. Choose the option that best fits the trend characteristics of your data.
  • The available options are: auto, flat, linear, logistic
• Exogenous
  • External factors or additional regressors that can be included to improve forecasting accuracy. These factors should be relevant to the target variable and can help enhance the model's predictions.
  • The available options are: Temperature, price, false
• Exogenous Selection
  • Select from the list of exogenous features the most relevant regressors that can be included in the forecasting model. This helps in improving the model's accuracy by including significant external factors.
  • The available options are: True or False
• Frequency
  • Describe time frequency. This hyperparameter defines the granularity of the time series data. Choose the option that matches the frequency of your data collection.
  • The available options are: Daily, Weekly, Monthly
• Algorithm
  • Choose the name of the algorithm to apply. The auto setting selects the appropriate algorithm based on the characteristics of the time series. Select the algorithm that best suits your data and forecasting needs.
  • The available options are: auto, prophet, croston, croston_tsb
• Holidays Prior Scale
  • Controls flexibility to fit holiday effects. This hyperparameter adjusts the model's sensitivity to holiday-related variations in the data. Choose a value that balances flexibility and accuracy.
  • The available range is: [0.01, 10]
• Changepoint Prior Scale
  • Determines the flexibility of the trend. Bigger values mean more flexibility. Too small values lead to underfitting, too large values lead to overfitting. Select a value that provides an optimal balance for your data.
  • The advised range us: [0.001, 0.05]
• Changepoint Range
  • By default, changepoints are inferred for the first 80% of the time series to avoid overfitting fluctuations at the end. Adjust this hyperparameter to control the portion of the time series used for changepoint detection.
  • The available range is: [0.8, 0.95]
• Seasonality Mode
  • Tuned by looking at the time series. Multiplicative if seasonality is not a constant additive factor. Choose the mode that best represents the seasonal patterns in your data.
  • The available options are: additive, multiplicative
• Seasonality Prior Scale
  • Controls the flexibility of the seasonality. Large values allow fitting large fluctuations, small values shrink the magnitude of the seasonality. Select a value that appropriately captures the seasonal variations in your data.
  • The available range is: [0.01, 10]
• Seasonalities
  • The default setting activates auto mode which automatically generates seasonalities according to the time-level of the data. Additionally, users have the option to specify seasonal components to incorporate into the modeling process. Seasonalities consist of period-Nterms pairs, where "period" refers to the periodicity of the Fourier terms, and "Nterms" represents the number of Fourier terms (cosine and sine). Users can include multiple seasonal components, which should be comma-separated. For example, "12-4,4-2,3-1" translates to [(12-4), (4, 2), (3, 1)].

    Example: [auto, 12-4,4-2,3-1]

• Croston
  • Parameters for the Croston model. The alpha value=0,7. This hyperparameter is used for intermittent demand forecasting.
  • The available range is: [0.001, 1]
• Croston TSB
  • Croston TSB model parameters, alpha and beta values, string of floats comma separated. These hyperparameters are used for enhanced intermittent demand forecasting.
  • The available range is: Values between 0 and 1
• Benchmark
  • Benchmark model parameters and name. For example, last year provides the last year observation as a forecast. For moving average, you need to set two parameters: window and offset. The hyperparameter values must be comma-separated. For example, moving average,6,365 means moving average(window=6, offset=365).
  • The available options are: moving average, last year, average