{'fraction_non_alphanumeric': 0.021674140508221227, 'fraction_numerical': 0.007473841554559043, 'mean_word_length': 4.746781115879829, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Holt-Winters Forecast on Seasonal Time Series Crop Insurance Data with Structural Outliers (2000-2018) Using Ms Excel

Time series data on the number of farmers enrolled in different crop insurance schemes such as NAIS, MNAIS and PMFBY in the state of Tamil Nadu, India was analysed for its seasonality, outliers and forecast. The data was found to be seasonal and exponentially increasing. Seasonality is innate as the crop insurance itself was registered separately for two seasons (Kharif and Rabi). Multiplicative Holt-Winters Model should be applied in order to do short range forecast for an exponentially increasing dataset. The model was run in Ms-Excel in order to understand the basics of times series forecasting. Minimum MSE value was the criteria used to find the better fitting smoothing values. The residual of the model was examined for the fit of the model. Residual mean value was close to zero. Residuals are tested for autocorrelation with Durbin-Watson test and Runs test. Histogram of residuals implies a normal distribution. Presence of outliers are detected using 3IQR method and the identified outliers are part of the structure of data and need not be removed. However, alternate models of Holt-Winters itself which are robust to work with outliers are reviewed and RHW model of Gelpers et al. [1] was suggested.